.predict() generates output predictions based on the input you pass it (for example, the predicted characters in the MNIST example)

.evaluate() computes the loss based on the input you pass it, along with any other metrics that you requested in the metrics param when you compiled your model (such as accuracy in the MNIST example)

model.compile(loss='categorical_crossentropy', 
 optimizer=RMSprop(), 
 metrics=['accuracy']) 
history = model.fit(x_train, y_train, 
 batch_size=batch_size, 
 epochs=epochs, 
 verbose=1, 
 validation_data=(x_test, y_test)) 
 
predictions = model.predict(x_test) 
print('First prediction:', predictions[0]) 
 
score = model.evaluate(x_test, y_test, verbose=0) 
print('Test loss:', score[0]) 
print('Test accuracy:', score[1]) 

In prediction, we don’t necessarily care why something happens or how each variable effects eachother. Let’s look at an example:

Say you work for a car insurance company and your boss tasks you with predicting if future clients will get in an accident. You look through the data, run some models, and find out that the lower a person’s credit score is, the higher the likelihood of him or her getting in a car accident is (this is true, by the way). Why is that? Who cares. It doesn’t matter. What matters is that there is a relationship between credit score and car accidents and we can better predict the outcome. So if a new customer comes in and has a low credit score, we know to raise their insurance prices. End of story.

In inference, however, we may care why something happens.

Say you work for a real estate company and your boss wants to know about housing prices and how they are effected. You get a data set from Zillow, which has a number of attributes to go along with housing prices, such as # of bedrooms, backyard size, etc. In inference, we would do a little more than just predicting the price of the house. We would want to say something like “For every extra 10 feet of backyard space, we can expect the price of a house to increase by $5000.” In this case, we care how the predictor variables effect the response, and may have to delve in to why that is.

Hope this helps.

I’ll answer it in a technical way.

An algorithm is a mathematical technique. An algorithm is derived by statisticians and mathematicians for a particular task i.e. in our case prediction. Algorithms in machine learning were derived many years ago. Only when they were implemented in the form of a code in a computer, the algorithms’ utility increased to a very great extent since the computers can handle high computation very easily.

Let me give you an example.

[math]y = w_{0} + w_{1}x[/math]

You might be knowing that this is an equation of a line, where [math]w_{0}[/math] corresponds to the y-intercept and [math]w_{1}[/math] corresponds to slope of the line.

This is nothing but the equation of linear regression with one variable.

Similarly every algorithm has some mathematical form underneath it, which when implemented in a machine developed to form a machine learning algorithm.

Now coming to defining a model.

In the above equation, you cannot find y if you don’t know w0 and w1. So how to find it? Suppose you are given a set of sample data, say 2 values of x and y, then certainly you can find the slope by slope-point form. Again let’s take the 2 points be [math](x_{1},y_{1}) = (1,1)[/math] and [math](x_{2},y_{2}) = (2,2)[/math]

Now by slope-point form we can find [math]w_{1}[/math] for which the formula is

[math]w_{1} = \dfrac{y_{1}-y_{2}}{x_{1}-x_{2}}[/math]

So, [math]w_{1} = -1[/math]

Now by substituting it in the above equation we can get [math]w_{0}=0[/math]

By all this calculation, we have an equation,

[math]y = 0 + (-1)x[/math]

This is a model.

So we can now say that a model is an equation which is formed by finding out the parameters ([math]w_{0}, w_{1}[/math]) in the equation of the algorithm. And you create a model using some data, in this case, the two points which we helped us calculate [math]w_{0},w_{1}[/math]. This is called training a model.

Now we can find any value of [math]y[/math] given a new value of [math]x[/math]. This is how prediction takes place using algorithms.

I hope now have clear idea of an algorithm and a model.

Model selection and evaluation are two critical steps in the machine learning pipeline. They help ensure that your models perform well on unseen data and can generalize effectively. Here's a simplified step-by-step guide to these processes:

1. Data Splitting: The first step is splitting your dataset into three parts: training set, validation set (also known as development set), and test set.

- Training Set: This is used to train the model.

- Validation Set: It's used to tune hyperparameters of the model and select features that are most relevant for prediction.

- Test Set: The final evaluation of your model, which should be a clean dataset not seen during training or validation.

2. Model Training: Train different models on the training set using various algorithms. You might use several different machine learning techniques depending on what you're trying to predict (e.g., regression, classification, clustering).

3. Hyperparameter Tuning: Use your validation set to tune hyperparameters of each model. Hyperparameters are settings that control the behavior of a machine learning algorithm and can be tuned for better performance. Examples include learning rate, number of iterations, regularization parameters etc.

4. Feature Selection/Extraction: Based on the results from your validation set, you may need to select or extract more relevant features for prediction. This step is often iterative as it involves a lot of trial and error.

5. Model Evaluation: After training and tuning models, evaluate them using metrics such as accuracy, precision, recall, F1-score, ROC curve etc., on the test set.

- Accuracy: The ratio of correctly predicted observations to total observations.

- Precision: It is a measure of result relevancy, i.e., of how many selected documents are relevant? High precision relates to low false positive rate.

- Recall (Sensitivity): It tells us about the completeness of our model’s prediction. If we have imbalanced data set and if we predict all negative instances as positive then recall will be high but it is not a good measure in this case.

- F1-score: The weighted average of Precision and Recall. It tries to find the balance between precision and recall.

- ROC curve (Receiver Operating Characteristic Curve): A plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.

6. Model Selection: Based on your evaluation, choose the model with the best performance measure(s) and lowest error rate. This could be based on accuracy, precision, recall, F1-score or any other metric you've decided to use.

7. Final Model Training & Deployment: Train the final chosen model using all of your training data (both features and labels), then deploy it for real-world predictions.

8. Continuous Learning/Model Refinement: Monitor the performance of your deployed models over time, and continually retrain them with new data to ensure they remain accurate as more information becomes available. This is known as continuous learning or online learning.

Prediction and detection in machine learning serve different purposes:

Prediction:

1. Estimates future values or outcomes
2. Often uses regression or time series models
3. Examples: stock prices, weather forecasts, user behavior

Detection:

1. Identifies presence or absence of specific patterns/objects
2. Typically uses classification or anomaly detection models
3. Examples: object detection in images, fraud detection, spam filtering

Key differences:

• Prediction forecasts future states; detection finds current patterns
• Prediction outputs continuous values; detection often binary (yes/no)
• Prediction models temporal relationships; detection focuses on spatial or feature-based patterns

Both can use similar algorithms, but they're applied differently based on the problem type and desired outcome.

I am brushing in broad strokes here.

Accuracy

Accuracy is the simplest metric and can be defined as the number of test cases correctly classified divided by the total number of test cases.

It can be applied to most generic problems but is not very useful when it comes to unbalanced datasets. For instance, if we’re detecting fraud in bank data, the ratio of fraud to non-fraud cases can be 1:99. In such cases, if accuracy is used, the model will turn out to be 99% accurate by predicting all test cases as non-fraud.

This is why accuracy is a false indicator of model health, and for such a case, a metric is required that can focus on the fraud data points.

Performance

The primary objective of model comparison and selection is definitely better performance of the machine learning software/solution. The objective is to narrow down on the best algorithms that suit both the data and the business requirements.

High performance can be short-lived if the chosen model is tightly coupled with the training data and fails to interpret unseen data. So, it’s also important to find the model that understands underlying data patterns so that the predictions are long-lasting and the need for re-training is minimal.

Sometimes it might happen that the training curve shows an improvement but the validation curve shows stunted performance. This is indicative of the fact that the model is overfitting and needs to be reverted to the previous iterations. In other words, the validation learning curve identifies how well the model is generalizing.

Great question! I always begin my first lecture of my graduate ML course with this question. I like analogies, so the best way to explain the answer is through an analogy.

ML is to statistics as engineering is to physics.

How does civil or electrical or mechanical engineering differ from physics? The latter is the study of fundamental laws of the universe, of matter, of conservation of energy and symmetry etc. The former engineering fields are attempts to build structures, gadgets, machines that build on the deep knowledge of the universe that physics gives us. It is laughable to think that we could have computers without the deep knowledge of material science that came from physics, particularly quantum mechanics. It was quantum theory that was used by the pioneering Bell Lab scientists in their first development of the transistor, a solid state switching device that was far superior to the older vacuum tube device. Without quantum mechanics, transistors could never have been developed. The N-P-N junction can only be explained by quantum effects, since it requires understanding how “holes” (gaps where electrons resided) could move across junctions.

Similarly, statistics is the science that underlies the modern effort to build “learning machines”, or machine learning. Statistics is the original data science, and it is somewhat ironic that ML researchers have wrapped themselves in this cloak of “data science”. Statisticians, for over a hundred years, have labored mightily to build the principles of data science. The deepest and most beautiful theorems in data science come not from machine learning, but from statistics.

Take the beautiful concept of “sufficient statistics”. What can you abstract from raw data so that you retain all the knowledge necessary about the generative model that can “explain” the data. The famous Rao-Blackwell theorem is an example of such a deep theorem, which can guide the design of powerful machine learning systems (and has done so, for decades).

Trying to do ML without knowing statistics is like to trying to build engineering structures without physics. You can certainly succeed in some ways — after all, the Egyptians built the pyramids — but it will be a risky trial and error exercise, possibly costing thousands of lives in costly mistakes. Science allows the engineer to construct safe designs, solutions that can be tested in simulation, and built reliably (such as the latest skyscraper in San Francisco, the new SalesForce building that has been extensively tested to withstand the next earthquake, whenever that happens).

Here are some of the key differences in prediction, and detection in machine learning:

Prediction:

1. Objective: Prediction involves forecasting a specific outcome or value based on input data. It aims to estimate a numerical or categorical result.
2. Examples: Predicting the price of a house based on its features, forecasting the weather, or predicting whether an email is spam or not.
3. Output: The output is typically a single value (e.g., a number, category) that represents the predicted result.

Detection:

1. Objective: Detection is about identifying the presence or absence of a specific object, feature, or event in the input data. It's a binary decision (yes/no) or multi-class classification.
2. Examples: Object detection in images (finding where an object is), anomaly detection in network traffic (identifying unusual behavior), and face detection in videos.
3. Output: The output is a binary or categorical label indicating whether the target object or event is detected or not.

In simple layman’s terms, a model is like a Vending Machine, which given an input (money), will give you some output (a soda can maybe).

An algorithm is what is used to train a model, all the decisions a model is supposed to take based on the given input, to give an expected output. For example, an algorithm will decide based on the dollar value of the money given, and the product you chose, whether the money is enough or not, how much balance you are supposed to get, and so on. ( I really have very little idea how these things work.)

Short answer

Validation is used to tune the hyper-parameters of the model and is done on the cross validation set.

Evaluation is used to test the final performance of the algorithm and is done on the test set.

Longer answer

When you are training a machine learning model, there are several hyper-parameters. For instance, when you are training a neural network, there are hyper parameters like:

• Depth of network
• Width of each layer
• Learning rate

Of course there are several weight parameters, but those are ‘parameters’. Here we are talking about ‘hyper-parameters’. Hyper parameters in some sense define the ‘structure’ of the machine learning model. For a weight parameter, you might have a huge set of choices, however, for the depth of the network, you have a few choices. For instance, for a simple deep network, you can try depth = 2 or 4 or 8.

In order to decide the value of hyper-parameters, the general process is to separate a part of the data given to us as cross validation data. We then choose a set of hyper parameters (say, depth = 1, width = 100, learning rate = 0.01) and train the network. We do this for all possible combinations of hyper parameters that we think are relevant. For instance, we can try for depth = 1, width = 50, learning rate = 0.01 as well. Generally, the possible combinations are not too many and in practice, we vary the hyper parameters in logarithmic scale. For instance, it makes sense to try learning rate = 0.01 and then learning rate = 0.1. Generally, we won’t try learning rate = 0.01 and 0.012 because those values are quite close and it is unlikely that we will get a significant difference in performance between the two.

We then run each of the models obtained on cross validation data and see which set of hyper-parameters gives us the best results. Finally, those set of hyper-parameters is chosen for the final model. This process of choosing hyper-parameters is called as validation.

Once this process has been done for sufficient number of times, final performance of the algorithm is tested on untouched test data to see how well the model is able to generalize. This is called as evaluation.

Each machine learning model tries to classify information and make a prediction accordingly.

So different characteristics of the training model can create different predictions. Each model tries to calculate current input’s highest similar class or cluster trained previously by the system.

Below picture explains the main idea for different learning types:

Footnotes

Machine learning (ML) offers several advantages for predictive modeling, making it a powerful tool in data analysis and decision-making. Here are some key benefits:

1. Improved Accuracy

ML models can detect complex patterns and relationships in data that traditional statistical methods may miss.

They can adapt and improve their predictions over time with more data.

2. Automation & Efficiency

Once trained, ML models can make predictions with minimal human intervention.

Automated feature selection and engineering can speed up model development.

3. Handling Large & Complex Data

ML can process vast amounts of structured and unstructured data, making it ideal for big data applications.

It excels in high-dimensional datasets where traditional models struggle

4. Adaptability & Scalability

ML models can continuously learn from new data, improving predictions dynamically.

They scale well with increasing data volume, making them suitable for real-time analytics.

5. Ability to Capture Non-Linear Relationships

Unlike linear regression models, ML techniques (e.g., decision trees, neural networks) can model complex, non-linear dependencies.

6. Versatility Across Industries

Used in finance (fraud detection, risk assessment), healthcare (disease prediction), marketing (customer segmentation), and more.

7. Better Handling of Missing or Noisy Data

Algorithms like Random Forest and Neural Networks can handle incomplete or noisy datasets better than traditional methods.

8. Improved Decision-Making

By leveraging historical data, ML enables data-driven decision-making, reducing reliance on intuition.

Retraining machine learning models on previously seen data may be necessary in several scenarios:

1. Data Drift

• Definition: This occurs when the statistical properties of the input data change over time. If the features used in the model no longer reflect the current environment, predictions may become less accurate.
• Action: Regularly monitor the data distribution and retrain the model when significant drift is detected.

2. Concept Drift

• Definition: This happens when the underlying relationship between input features and the target variable changes. For instance, in predictive maintenance, the factors leading to machine failure might evolve.
• Action: Retraining is essential to ensure that the model adapts to new patterns in the data.

3. Model Performance Degradation

• Definition: If the model's performance metrics (like accuracy, precision, or recall) drop significantly over time, it may indicate that the model is no longer relevant.
• Action: Analyze performance metrics regularly and retrain the model when a drop is observed.

4. Incorporating New Data

• Definition: As more data becomes available, particularly if it represents new trends or patterns, retraining can help the model learn from this additional information.
• Action: Periodically update the model with new data to improve its accuracy and robustness.

5. Algorithm Updates or Changes

• Definition: If there are improvements in algorithms or if new techniques become available, retraining the model with updated methodologies can enhance performance.
• Action: When deploying a new algorithm version, consider retraining the model on existing data.

6. Regulatory or Compliance Changes

• Definition: Changes in laws or regulations may require models to be updated to ensure compliance, especially in fields like finance or healthcare.
• Action: Ensure models are retrained or adjusted to meet new compliance standards.

7. Feature Engineering Changes

• Definition: If the features used in the model are altered or new features are introduced based on insights from data analysis, retraining is necessary to capture these changes.
• Action: Update the model to incorporate new or modified features to maintain performance.

8. Feedback Loops

• Definition: In some applications, model predictions can influence future data (like recommendations affecting user choices). This can lead to shifts in data patterns.
• Action: Regularly retrain the model to account for these changes.

Summary

Retraining machine learning models is crucial in maintaining their accuracy and relevance in a changing environment. By monitoring for signs of data and concept drift, performance degradation, and other factors, organizations can ensure that their models remain effective over time.

4o mini

The machine learning model is just a fancy word used for mathematical function. As most of us are familiar, a math function typically comprises an input, an output, intermediate operations, a few constants, and variables. While training the model, an output is produced using the machine learning model (or a function), a loss is calculated (difference compared to the actual value), and the feedback is backpropagated to update the variables. The model is repeated until the loss is minimized and optimum values for the variables are found.

However, model predictions only mean the output produced by the model (or a function) when given some input. If the model weights or variables are adjusted well, the prediction would be close to the expectation. Otherwise, the model might produce some random output.

I think there isn’t much of a difference (at least conceptually). Inference means estimating the values of some (usually hidden random) variable given some observation. This is usually in a PGM context. Prediction is usually in a supervised learning context where given some data point we predict (say for e.g.) some label y. In this context y is a (hidden variable) we are predicting from visible data x (and probably many other examples e.g. a data set). So in a sense they are the same. Both require estimating the value of something hidden from known data.

For an interesting difference, in a PGM one can estimate the whole model with data (i.e. some graph of variables connected in some hopefully useful way). Once the actual model is “known” then one can just run the inference engine on it. What that means is that given any set of observations of the variables of the model one can (in principle even if its computationally hard) predict any posterior distribution of the model. Usually inference is computing P(Y|X) where X and Y are any set of variables (and with that do MAP, the most likely posterior if you want it to look “predictive” like a discriminate model, i.e. give you an actual concrete value). This type of flexible prediction/inference engine is possible on a PGM but not so simple in a supervised/discriminate model.

So to finish up maybe inference can be considered just a tiny bit more general since it computes a posterior P(Y|X) and usually a prediction simple involves getting a single value (like MAP). But P(Y|X) is still a prediction in my head, just with the additional benefit of a confidence distribution on your belief state.

Hope it helps.

(also as you can tell its also a way to use the language/word in context. Prediction is more for supervised models while inference is usually used more for generative models)

In Machine Learning, numerous evaluation metrics exist to help assess a model's accuracy and performance. For instance, there are metrics for classification, regression, clustering cases, etc. Using metrics for model performance evaluation makes it possible to understand and improve a model’s predictive power before you roll it out for production on new/fresh data.

When selecting a metric, it's essential always to have the end goal (primarily business goal) of the ML application. This is because ML is not just about creating models with high accuracy but enabling accurate predictions that are part of a more extensive decision-making business process. With that understanding, it is also crucial to know how to choose the right evaluation metric when selecting between models.

For example, you’ve got to ensure that the metric you choose for evaluating a model is ideal for what the model will be used for.

Take the case of a classification problem whereby the accuracy score is a simple and popular metric to assess a classifier. However, it will give you skewed results when there’s a data imbalance. Real-world classification problems often have unbalanced samples. In such a case, you have to apply other metrics such as precision, recall, f1-measure, etc., to evaluate your performance better.

In my experience, I have found it helpful to know and understand different metrics for different problems, understand the consequences, and pick an evaluation metric accordingly.

Also, tools, e.g., deepchecks that help with quick data validation and model evaluation checks without you doing everything from scratch, are important when they are part of a data scientist’s or ML engineer's toolbox.

Happy learning! Wish you success.

As another answer stated, a neural network is a kind of machine learning model.

Machine learning models are not particularly amazing at forecasting because, well, forecasting without any a priori assumptions is damn hard (the future and the present need not have any dependable relationship). That being said, if you really want to take a crack at prediction you should use a time series model and derive confidence interval bounds to cover your ass. If you actually know that future data will be dependent on past data in a complicated way (say, as in language modeling or in sequential pattern recognition) then an RNN (recurrent neural network) such as an LSTM or GRU (Long Short Term Memory and Gated Recurrent Unit, respectively) with adequate regularization measures might fit the bill.

Note that a single layer neural network with a logistic activation function is merely performing logistic regression. If you want to exploit the “universal function approximating” properties of NN’s without needing billions of parameters, you will need an NN with multiple stacked layers (i.e. a “deep” neural network). However, note that along with this property comes the nasty tendency to overfit training data. Make sure to implement regularization/optimization schemes such as batch gradient descent, batch normalization, Dropout, etc. Finally, data is the biggest regularizer, and if you don’t have enough of it for your problem/model, then your NN is screwed no matter what tricks you use.

It depends on the algorithm used. Many algorithms are passive in nature and do not make any changes to the model when an example is already correctly dealt with. For such algorithms, incorrect predictions are the most informative.

When talking about learning in the most abstract sense, we can consider Version Spaces. This is an approach that keeps all models that are compatible with all seen examples so far. Already correctly labeled examples have no value whatsoever for this type of approach.

Another approach is the field of Active Learning. Here the algorithm decides which examples it receives the correct target values for. Here, again, the algorithm will never choose any example for which it is already absolutely certain. Of course, there is an entire space of examples for which the algorithm might have a correct prediction at the moment but is unaware of it being correct. For these examples, correct predictions do have some values.

There are other approaches where already correctly labeled examples reinforce the model.

So, in short, it completely depends on a few factors.

I presume that you are well aware of what Machine Learning is, and you're probably thinking Deep Learning is a subset of ML, and pondering what's new in Deep Learning.

Well, Yes and No.

Machine Learning: (Recap)
In a nutshell, ML is field of Computer science that uses statistical (or mathematical) techniques to construct a model (or system) from observed data rather than have user enter specific set of instructions that define the model for that data.

Though the name seems fancy, sometimes it is as simple as linear regression (in very rudimentary form). A little more complex example is spam detector in your mailbox that "learns" which emails are spam, since you never gave instructions for each and every type of email.

Loosely speaking, most often, these algorithms work on precise set of features extracted from your raw data. Features could be very simple, such as pixel values for images, temporal values for a signal, or complex features such as Bag of Words feature representation for text. Most known ML algorithms only work as good as the features represent the data. Correct feature identification is that is close representative of your all states of your data is a crucial step.

What's the big deal about feature extractor ?
Making correct feature extractor is great deal of science in it self. Most of these features extractors (from data) are very specific in function and utility. For ex: for face detection one needs a feature extractor which correctly represents parts of face, resistant to spatial aberrations etc. Each and every type of data and task may have its own class of feature extraction. ( Ex: Speech Recognition, Image Recognition)

These feature extractors can then be used to extract correct data features for a given sample, and pass this information to a classifier/ predictor.

How is Deep Learning different ?
Deep Learning is broader family of Machine Learning methods that tries to learn high level features from the given data. Thus, the problem it solves is reducing task of making new feature extractor for each and every type of data (speech, image etc.)

For last example, Deep Learning algo's will try to learn features such as distance between eyes, length of nose etc. features when image recognition task is presented to them. They may use this info for classification, prediction etc tasks. Thus, this is a major step away from previous "Shallow Learning Algorithms."

Prof. Andrew Ng remarks that Deep Learning focuses on original aim of One Learning, an ideal Algorithm envisioned for an AI.

Long Story Short:
If you write this:
F(1,2,3.......,100) = 5050
and give it to a ML Algo, then that Algo is like the child which immediately understands that RHS is sum of all numbers in LHS. Given new set of numbers F(1,2,3.......,500) , the childwill then sum up all the numbers upto 500.

Then, Deep Learning Algo's are like Carl Fredrich Gauss, which will realize that sum of ith element from forward and backward is always fixed, and can be used to find the sum.

Ok here goes.

Key is to note that an algorithm is a generic sequence of steps with flow and loops etc transforming input to output.

In general algorithm is implemented in a program written in a programming language. In context of a machine learning program, a algorithm like say the gradient descent algorithm for linear regression takes an input set of data and outputs a equation which is a model. Model is in some sense an executable which is output of the machine learning algorithm.

A model is then used as the deployment entity which takes any input in future and produces an output prediction.

So Algorithm in machine learning is used to produce an output deployable executable Model, which can be used in future to predict values.

first, let’s give a quick intuition about statistics,

in the beginning of statistics , we had only two parts (descriptive and inferential), why? because we were (and we still) unable to collect every single data in the world across all times to find an overall measurement or describe precisely a relationship between some variables, so the solution was to collect as much data as we can which we call a sample, then try to give a descriptive analysis, say how the data set looks like using graphics (univariate, multivariate) and summary measures like mean, variance…etc.

but that’s not enough, we want to know if these measures are valid for the unseen data (data we were not able to collect) and if not, we want to extrapolate these measures to estimate (infer) general measures in the whole population we care about, to do so, we use some statistical rules like (test hypothesis, confidence interval… etc) and this is the inferential part. But if you do further researches about this field, you’ll find that we always suppose that data follow a particular shape like normality and independence, and all results we get depend on these hypothesis. and you can easily imagine that this is a limited analytical view.

but, what about prediction ?

to predict an unseen data, we need a generalized formula, a general relationship estimated basing on the previous inferential rules, this is the case for SVM, linear and logistic regression. A big BUT here, is that the recent machine learning algorithms like neural network seem to not follow this chart. Well you’re right, thanks to the computing power and high performance of recent computers, we’re able to go beyond the traditional hypothesis, we can handle the data like it is, without any restriction, the only problem is that we don’t know how it gets to learn (estimate the generalized formula) that’s why we call the learning process as the black box.

hope this is helpful.

Machine learning models cannot achieve 100% accuracy due to the inherent complexity and variability of real-world data. Data often contains noise, outliers, and inconsistencies that can lead to errors in predictions. Additionally, the models are trained on historical data, which may not fully represent future scenarios or unseen data distributions, resulting in limitations in their predictive capabilities.

Another factor is the bias-variance tradeoff, where models that are too simple may underfit the data, while overly complex models may overfit, capturing noise rather than the underlying patterns. This balance is crucial for generalization, and achieving perfect accuracy would require an unrealistic level of model complexity and data representation that is seldom feasible.

The concept of accuracy itself can be misleading, especially in imbalanced datasets where one class significantly outnumbers another. A model could achieve high accuracy by simply predicting the majority class, but this would not reflect its true performance across all classes. Thus, relying solely on accuracy as a metric can obscure the model's effectiveness.

Ethical considerations and the unpredictability of human behavior also play a role in the limitations of machine learning models. In applications such as healthcare or finance, the consequences of errors can be significant, making it essential to prioritize reliability and interpretability over absolute accuracy.

The AI-News information provided here is for informational purposes only and should not be considered as financial, medical, or legal advice. Always conduct your own research and consult with qualified professionals before making any decisions related to financial investments, medical treatments, or legal matters. The content shared here is not intended to be a recommendation for any specific course of action.

I interpret your question as do ML algorithms learn More (per example) or Faster with “Diverse” training examples or with “more Prototypical / similar examples”. The latter is sometimes also referred to as “redundancy” in the training data.

I think it is a fine line. Inducing diversity can help or hurt:

1. Can speed up the learning (per example). This can be useful when :
1. training algo would otherwise take a long time to train
2. we expect information redundancy across training rows (e.g. 100 almost identical images of a “dog” in dog-cat classifier)
2. But if taken to extreme, we are making the learning process:
1. more Difficult
2. more likely to focus on learning Exceptions instead of the Rule (unexpected/ diverse examples are more likely to be Outliers in many situations)
3. (also need to ensure that we are not deviating the Training Distribution from the Generalization Distribution when implementing such designs)

By way of loose Analogy to Human Learning process… In school, would kids learn the general concept of how to add two numbers faster:

• if one teacher uses only very tough & diverse examples (e.g. what is 1 + 5? 499 + 340? 0+ 0? in that sequence of training)
• OR if another teacher uses prototypical examples similar to previously learnt ones (e.g. what is 1 + 1? 1 + 2? 1 + 3? 1 + 4? 1 + 5? in that sequence of training)

LeCun mentions this in the paper “Efficient BackProp”, in the context of possible shuffling strategies when training a Deep learning model. Section 4.2. http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf

Model predictive control (MPC) is a control method; machine learning (ML) (viewed generally) is not a control method (note that ML can be used to develop control methods).

MPC, as the name suggests, is a family of “model-based” control methods based on real-time repeated optimal control, developed for solving multivariable, constrained, and possibly nonlinear, optimal control problems. Notice here that “model” has a very specific meaning: It means the mathematical model (usually a set of differential equations) of a dynamical system. Although MPC also makes use of mathematical optimization, this does not mean that it is “a specific method of applying ML” to control. The two families of methods (i.e., MPC and ML) use optimization for distinct purposes: Being a non-expert in ML, as far as I can understand, ML is about improving algorithms using data and optimization so that they make good predictions/decisions on new data. However, in its standard form, an MPC algorithm does not improve itself through data, it simply optimizes future system trajectories through predictions using the dynamical model of the system in question (thus, I have to kindly disagree with Håkon Hapnes Strand’s answer).

Anything where data cannot be predicted reliably without a certain set of relevant parameters.

We base most of these predictions from Statistics (the 2nd-level kind) Your regressions analysis from Statistics 101 like Hypothesis-testing is just the basics… When we perform statistical analysis we perform techniques like (Boosting, Lasso…etc) these are not taught in statistics 101.

Data is fundamental to making predictions. If you were going to perform trend, scatterplots, histograms, graphical simulations.. Machine learning can only be processed through reliable data.

Machine learning goes through a series of processes in Neural Networks having an Input, Hidden (Computation graph) and output layer.

Such computation are based off on the accuracy and reliability of the data… Corruption of the data could mean WHEN DID it HAPPEN? “during before, in duration or after”. When we process data, of which we can’t predict accurately or reliably from this information. Estimations can be performed with EM Methods (Expectation-Maximization), MLE (Estimation-Maximum likelihood calculations)

Machine Learning therefore goes through a series of steps understanding how to classify the data (Unsupervised or Supervised) into Global sectors (K-Means) or Local (K-Nearest neighbours).

Machine learning - Wikipedia

As from the graph, you can see there is a Hyperplane, the nearer it is to the hyperplane, the more relevant to your hypothesis it becomes. There are certain rules that applies here too.. Using the ‘line of best fit’ we can ascertain where and how accurate these measurements really are to our hypothesis.

So, in all that’s been surmised… This is just an observation to your question… Without reliable data, we can’t predict the outcomes WELL.. But with most things, there will always be DATA. And as such we can ALWAYS PREDICT as there are calculations which will allow us to do so.

If the feature set of the data you want to make predictions for is a superset of the features of the training set, you’ll be fine; just ignore the other features.

If not, you’re dealing with missing data. Some machine learning approaches can still work if you have missing data, but you’ll need to look up which ones can safely be used in your setting. Look into the documentation, and make sure to read the referenced papers carefully. You might want to look into imputation models, although that can be very risky for some settings.

You’ll probably want to sit down with the subject matter experts and discuss what assumptions you can safely make when dealing with missing data in this particular project.

“Machine learning models are homogeneous to functions that will predict some output for a particular given input.”

In order to generate ML Model, we need:

1. Sample Data with target attribute given.
2. ML Algorithm chosen according to the nature of target attribute.

Process:

• Input the training dataset.
• Let the machine learning algorithm run on the data. [ The algorithm now learns and captures the pattern in the data]
• Tune the parameters to control the learning of the algorithm. [ To facilitate accuracy]
• After the algorithm finishes learning, the model is finally built.

Now, when a new dataset comes in for prediction, it is passed to the model. The model that is built by learning the past sample data, thus predicts the output.

Eg: Consider we have to predict what is the price of the house

1. The sample data will contain the attribute like area, no.of rooms, type and also value for PRICE [target attribute - which we have to predict for new data in future].
2. We will choose Linear Regression algorithm (y = mx + c) as the target attribute "Price" is numeric.

The algorithm will learn from the data and captures the pattern that, for such areas, no .of rooms and types, the price will be this.

Now, when a new data comes for prediction, it is directly sent to the model that will tell what will be the price of the house. [ as it has learned from the past sample data]

P.S : You can generate a new model with the same algorithm and different dataset (or) different algorithm and same dataset to achieve the accuracy/best prediciton.