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.
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.
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'.
As an extreme example, if the number of parameters is the same as or greater than the number of observations, then a model can perfectly predict the training data simply by memorizing the data in its entirety.
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.
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).
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.
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 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.
With a large set of explanatory variables that actually have no relation to the dependent variable being predicted, some variables will in general be spuriously found to be statistically significant and the researcher may thus retain them in the model, thereby overfitting the model.
Replacing this simple function with a new, more complex quadratic function, or with a new, more complex linear function on more than two dependent variables, carries a risk: Occam's razor implies that any given complex function is a priori less probable than any given simple function.
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.
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.
an underfitted model would ignore some important replicable (i.e., conceptually replicable in most other samples) structure in the data and thus fail to identify effects that were actually supported by the data.
Memorizing is not learning! — 6 tricks to prevent overfitting in machine learning.
Instead of learning the genral distribution of the data, the model learns the expected output for every data point.
The tricky part is that, at first glance, it may seem that your model is performing well because it has a very small error on the training data.
To test this ability, a simple method consists in splitting the dataset into two parts: the training set and the test set.
With this split we can check the performance of the model on each set to gain insight on how the training process is going, and spot overfitting when it happens.
The reason is that, as you add more data, the model becomes unable to overfit all the samples, and is forced to generalize to make progress.
Collecting more examples should be the first step in every data science task, as more data will result in an increased accuracy of the model, while reducing the chance of overfitting.
By progressively reducing its complexity — # of estimators in a random forest, # of parameters in a neural network etc. — you can make the model simple enough that it doesn’t overfit, but complex enough to learn from your data.
4 Reasons Your Machine Learning Model is Wrong (and How to Fix It)
When we build these models, we always use a set of historical data to help our machine learning algorithms learn what is the relationship between a set of input features to a predicted output.
This is bad because your model is not presenting a very accurate or representative picture of the relationship between your inputs and predicted output, and is often outputting high error (e.g.
If your model has low error in the training set but high error in the test set, this is indicative of High Variance as your model has failed to generalize to the second set of data.
If you can generate a model with overall low error in both your train (past) and test (future) datasets, you’ll have found a model that is “Just Right” and balanced the right levels of bias and variance.
If we were to train a machine learning model and it learned to always predict an email as not spam (negative class), then it would be accurate 99% of the time despite never catching the positive class.
Another way to interpret the difference between Precision and Recall, is that Precision is measuring what fraction of your predictions for the positive class are valid, while Recall is telling you how often your predictions actually capture the positive class.
The goal of a good machine learning model is to get the right balance of Precision and Recall, by trying to maximize the number of True Positives while minimizing the number of False Negatives and False Positives (as represented in the diagram above).
Plotting model error as a function of the number of input features you are using (see figure above), we find that more features leads to a better fit in the model.
If your model is overfit to the training data, it’s possible you’ve used too many features and reducing the number of inputs will make the model more flexible to test or future datasets.
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- On Sunday, March 24, 2019
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