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classifier overfitting

machine learning - (overfitting|overtraining|robust|generalization) (underfitting)

machine learning - (overfitting|overtraining|robust|generalization) (underfitting)

When your learner outputs a classifier that is 100% accurate on the training data but only 50% accurate on test data, when in fact it could have output one that is 75% accurate on both, it has overfit.

Model complexity decreases prediction error until a point (the bias trade-off) where we are adding just noise. The trainer error goes down as it has to, but the test error is starting to go up. That's over fitting.

If the number of parameters is the same as or greater than the number of observations, a simple model or learning process can perfectly predict the training data simply by memorizing the training data in its entirety, but such a model will typically fail drastically when making predictions about new or unseen data, since the simple model has not learned to generalize at all.

how to identify overfitting machine learning models in scikit-learn

how to identify overfitting machine learning models in scikit-learn

An analysis of learning dynamics can help to identify whether a model has overfit the training dataset and may suggest an alternate configuration to use that could result in better predictive performance.

Performing an analysis of learning dynamics is straightforward for algorithms that learn incrementally, like neural networks, but it is less clear how we might perform the same analysis with other algorithms that do not learn incrementally, such as decision trees, k-nearest neighbors, and other general algorithms in the scikit-learn machine learning library.

We care about overfitting because it is a common cause for poor generalization of the model as measured by high generalization error. That is error made by the model when making predictions on new data.

A plot of the model performance on the train and test set can be calculated at each point during training and plots can be created. This plot is often called a learning curve plot, showing one curve for model performance on the training set and one curve for the test set for each increment of learning.

The common pattern for overfitting can be seen on learning curve plots, where model performance on the training dataset continues to improve (e.g. loss or error continues to fall or accuracy continues to rise) and performance on the test or validation set improves to a point and then begins to get worse.

One approach for performing an overfitting analysis on algorithms that do not learn incrementally is by varying a key model hyperparameter and evaluating the model performance on the train and test sets for each configuration.

Shallow decision trees (e.g. few levels) generally do not overfit but have poor performance (high bias, low variance). Whereas deep trees (e.g. many levels) generally do overfit and have good performance (low bias, high variance). A desirable tree is one that is not so shallow that it has low skill and not so deep that it overfits the training dataset.

The expectation is that as the depth of the tree increases, performance on train and test will improve to a point, and as the tree gets too deep, it will begin to overfit the training dataset at the expense of worse performance on the holdout test set.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

A good example of this is varying the number of neighbors for the k-nearest neighbors algorithms, which we can implement using the KNeighborsClassifier class and configure via the n_neighbors argument.

We can perform the same analysis of the KNN algorithm as we did in the previous section for the decision tree and see if our model overfits for different configuration values. In this case, we will vary the number of neighbors from 1 to 50 to get more of the effect.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

The reason we do this is that in predictive modeling, we are primarily interested in a model that makes skillful predictions. We want the model that can make the best possible predictions given the time and computational resources we have available.

In general, if we cared about model performance on the training dataset in model selection, then we would expect a model to have perfect performance on the training dataset. Its data we have available; we should not tolerate anything less.

As we saw with the KNN example above, we can achieve perfect performance on the training set by storing the training set directly and returning predictions with one neighbor at the cost of poor performance on any new data.

A corollary is that a model that performs well on the test set but poor on the training set is lucky (e.g. a statistical fluke) and a model that performs well on the train set but poor on the test set is overfit.

Your blogs is really great and very informative. I am curious and want to know that in your blogs rarely you use real world datasets. In my opinion real world datasets make more sense and provide bigger picture. Majority of bloggers will use iris dataset or some random numbers 0f 10000, how that can be correlated to real world scenario. You are PhD & i believe you can do better job.

But when do you think it is very relevant? Maybe, when youre trying to spot overfitting. But lets say e_test

Hello! If I am considering, lets say, 1000 models, is it possible that the model that performs the best on the test set is accidentally overfit to the test set? And I would have chosen a different model on another unseen test set?

Its often said that the golden rule of machine learning is that the test data should not influence the learning process in any way. But in the example involving a decision tree classifier, you used the test set in order to tune the max_depth hyper-parameter. Doesnt this violate that golden rule?

Yes, I intentionally reuse data in some cases to keep the algorithm examples simple and easy to understand: https://machinelearningmastery.com/faq/single-faq/why-do-you-use-the-test-dataset-as-the-validation-dataset

machine learning - how to check for overfitting with svm and iris data? - data science stack exchange

machine learning - how to check for overfitting with svm and iris data? - data science stack exchange

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I am using machine learning predictions for the sample iris dataset. For instance, I am using the support vector machines (SVMs) from scikit-learn in order to predict the accuracy. However, it returns an accuracy of 1.0. Here is the code I am using:

You check for hints of overfitting by using a training set and a test set (or a training, validation and test set). As others have mentioned, you can either split the data into training and test sets, or use cross-fold validation to get a more accurate assessment of your classifier's performance.

This can be done using either the cross_validate or cross_val_score function; the latter providing multiple metrics for evaluation. In addition to test scores the latter also provides fit times and score times.

Of course the iris dataset is a toy example. On larger real-world datasets you are likely to see your test error be higher than your training error, with cross-validation providing a lower accuracy than the raw number.

So I wouldn't use the iris dataset to showcase overfitting. Choose a larger, messier dataset, and then you can start working towards reducing the bias and variance of the model (the "causes" of overfitting).

Based on here, use sklearn.model_selection.train_test_split(*arrays, **options) in order to split your data into train and test. Train your model on train-split and use the predict method to see the performance on the test data. As an example take a look at the following code which splits the data to two separate groups.

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overfitting in machine learning: what it is and how to prevent it

overfitting in machine learning: what it is and how to prevent it

However, if you could only sample one local school, the relationship might be muddier. It would be affected by outliers (e.g. kid whose dad is an NBA player) and randomness (e.g. kids who hit puberty at different ages).

In standard k-fold cross-validation, we partition the data into k subsets, called folds. Then, we iteratively train the algorithm on k-1 folds while using the remaining fold as the test set (called the holdout fold).

It wont work every time, but training with more data can help algorithms detect the signal better. In the earlier example of modeling height vs. age in children, its clear how sampling more schools will help your model.

An interesting way to do so is to tell a story about how each feature fits into the model. This is like the data scientist's spin on software engineers rubber duck debugging technique, where they debug their code by explaining it, line-by-line, to a rubber duck.

If anything doesn't make sense, or if its hard to justify certain features, this is a good way to identify them. In addition, there are several feature selection heuristics you can use for a good starting point.

The method will depend on the type of learner youre using. 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.

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