AI News, Overfitting in Machine Learning: What It Is and How to Prevent It

Overfitting in Machine Learning: What It Is and How to Prevent It

Did you know that there’s one mistake… …that thousands of data science beginners unknowingly commit?

But don’t worry: In this guide, we’ll walk you through exactly what overfitting means, how to spot it in your models, and what to do if your model is overfit.

Next, we try the model out on the original dataset, and it predicts outcomes with 99% accuracy… wow!

When we run the model on a new (“unseen”) dataset of resumes, we only get 50% accuracy… uh-oh!

In predictive modeling, you can think of the “signal” as the true underlying pattern that you wish to learn from the data.

If you sample a large portion of the population, you’d find a pretty clear relationship: This is the signal.

it has too many input features or it’s not properly regularized), it can end up “memorizing the noise” instead of finding the signal.

In statistics, goodness of fit refers to how closely a model’s predicted values match the observed (true) values.

model that has learned the noise instead of the signal is considered “overfit” because it fits the training dataset but has poor fit with new datasets.

Underfitting occurs when a model is too simple – informed by too few features or regularized too much – which makes it inflexible in learning from the dataset.

too complex (high variance) is a key concept in statistics and machine learning, and one that affects all supervised learning algorithms.

key challenge with overfitting, and with machine learning in general, is that we can’t know how well our model will perform on new data until we actually test it.

For example, it would be a big red flag if our model saw 99% accuracy on the training set but only 55% accuracy on the test set.

Then, we iteratively train the algorithm on k-1 folds while using the remaining fold as the test set (called the “holdout fold”).

It won’t work everytime, but training with more data can help algorithms detect the signal better.

This is like the data scientist's spin on software engineer’s rubber duck debugging technique, where they debug their code by explaining it, line-by-line, to a rubber duck.

For example, you could prune a decision tree, use dropout on a neural network, or add a penalty parameter to the cost function in regression.

Bagging uses complex base models and tries to 'smooth out' their predictions, while boosting uses simple base models and tries to 'boost' their aggregate complexity.

While these concepts may feel overwhelming at first, they will ‘click into place’ once you start seeing them in the context of real-world code and problems.


In statistics, overfitting is 'the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably'.[1] An overfitted model is a statistical model that contains more parameters than can be justified by the data.[2] The essence of overfitting is to have unknowingly extracted some of the residual variation (i.e.

The potential for overfitting depends not only on the number of parameters and data but also the conformability of the model structure with the data shape, and the magnitude of model error compared to the expected level of noise or error in the data.[citation needed] Even when the fitted model does not have an excessive number of parameters, it is to be expected that the fitted relationship will appear to perform less well on a new data set than on the data set used for fitting (a phenomenon sometimes known as shrinkage).[2] In particular, the value of the coefficient of determination will shrink relative to the original data.

The basis of some techniques is either (1) to explicitly penalize overly complex models or (2) to test the model's ability to generalize by evaluating its performance on a set of data not used for training, which is assumed to approximate the typical unseen data that a model will encounter.

Anderson, in their much-cited text on model selection, argue that to avoid overfitting, we should adhere to the 'Principle of Parsimony'.[3] The authors also state the following.[3]:32-33 Overfitted models … are often free of bias in the parameter estimators, but have estimated (and actual) sampling variances that are needlessly large (the precision of the estimators is poor, relative to what could have been accomplished with a more parsimonious model).

In regression analysis, overfitting occurs frequently.[5] In the extreme case, if there are p variables in a linear regression with p data points, the fitted hyperplane will go exactly through every point.[6] A study in 2015 suggests that two observations per independent variable are sufficient for linear regression[7].

In the process of regression model selection, the mean squared error of the random regression function can be split into random noise, approximation bias, and variance in the estimate of the regression function, and the bias–variance tradeoff is often used to overcome overfit models.

If the new, more complicated function is selected instead of the simple function, and if there was not a large enough gain in training-data fit to offset the complexity increase, then the new complex function 'overfits' the data, and the complex overfitted function will likely perform worse than the simpler function on validation data outside the training dataset, even though the complex function performed as well, or perhaps even better, on the training dataset.[11] When comparing different types of models, complexity cannot be measured solely by counting how many parameters exist in each model;

For example, it is nontrivial to directly compare the complexity of a neural net (which can track curvilinear relationships) with m parameters to a regression model with n parameters.[11] Overfitting is especially likely in cases where learning was performed too long or where training examples are rare, causing the learner to adjust to very specific random features of the training data, that have no causal relation to the target function.

Overfitting and Underfitting With Machine Learning Algorithms

The cause of poor performance in machine learning is either overfitting or underfitting the data.

Supervised machine learning is best understood as approximating a target function (f) that maps input variables (X) to an output variable (Y).

Induction refers to learning general concepts from specific examples which is exactly the problem that supervised machine learning problems aim to solve.

There is a terminology used in machine learning when we talk about how well a machine learning model learns and generalizes to new data, namely overfitting and underfitting.

This is good terminology to use in machine learning, because supervised machine learning algorithms seek to approximate the unknown underlying mapping function for the output variables given the input variables.

calculating the residual errors), but some of these techniques assume we know the form of the target function we are approximating, which is not the case in machine learning.

Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.

If we train for too long, the performance on the training dataset may continue to decrease because the model is overfitting and learning the irrelevant detail and noise in the training dataset.

The sweet spot is the point just before the error on the test dataset starts to increase where the model has good skill on both the training dataset and the unseen test dataset.

This is often not useful technique in practice, because by choosing the stopping point for training using the skill on the test dataset it means that the testset is no longer “unseen”

Overfitting is such a problem because the evaluation of machine learning algorithms on training data is different from the evaluation we actually care the most about, namely how well the algorithm performs on unseen data.

There are two important techniques that you can use when evaluating machine learning algorithms to limit overfitting: The most popular resampling technique is k-fold cross validation.

It allows you to train and test your model k-times on different subsets of training data and build up an estimate of the performance of a machine learning model on unseen data.

After you have selected and tuned your machine learning algorithms on your training dataset you can evaluate the learned models on the validation dataset to get a final objective idea of how the models might perform on unseen data.

Validation and overfitting

Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales’ forecasting and computer vision to name a few.

Be taught advanced feature engineering techniques like generating mean-encodings, using aggregated statistical measures or finding nearest neighbors as a means to improve your predictions. -

Be able to form reliable cross validation methodologies that help you benchmark your solutions and avoid overfitting or underfitting when tested with unobserved (test) data.

This course will teach you how to get high-rank solutions against thousands of competitors with focus on practical usage of machine learning methods rather than the theoretical underpinnings behind them. Prerequisites:

Train/Test Split and Cross Validation in Python

I’ll explain what that is — when we’re using a statistical model (like linear regression, for example), we usually fit the model on a training set in order to make predications on a data that wasn’t trained (general data).

As mentioned, in statistics and machine learning we usually split our data into to subsets: training data and testing data (and sometimes to three: train, validate and test), and fit our model on the train data, in order to make predictions on the test data.

We don’t want any of these things to happen, because they affect the predictability of our model — we might be using a model that has lower accuracy and/or is ungeneralized (meaning you can’t generalize your predictions on other data).

Let’s see what under and overfitting actually mean: Overfitting means that model we trained has trained “too well” and is now, well, fit too closely to the training dataset.

Basically, when this happens, the model learns or describes the “noise” in the training data instead of the actual relationships between variables in the data.

In contrast to overfitting, when a model is underfitted, it means that the model does not fit the training data and therefore misses the trends in the data.

The training set contains a known output and the model learns on this data in order to be generalized to other data later on.

Let’s load in the diabetes dataset, turn it into a data frame and define the columns’ names: Now we can use the train_test_split function in order to make the split.

Now we’ll fit the model on the training data: As you can see, we’re fitting the model on the training data and trying to predict the test data.

Here is a summary of what I did: I’ve loaded in the data, split it into a training and testing sets, fitted a regression model to the training data, made predictions based on this data and tested the predictions on the test data.

What if one subset of our data has only people from a certain state, employees with a certain income level but not other income levels, only women or only people at a certain age?

Here is a very simple example from the Sklearn documentation for K-Folds: And let’s see the result — the folds: As you can see, the function split the original data into different subsets of the data.

Because we would get a big number of training sets (equals to the number of samples), this method is very computationally expensive and should be used on small datasets.

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