Unbalanced classification using RandomForestClassifier in sklearn

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I have a dataset where the classes are unbalanced. The classes are either '1' or '0' where the ratio of class '1':'0' is 5:1. How do you calculate the prediction error for each class and the rebalance weights accordingly in sklearn with Random Forest, kind of like in the following link: http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#balance

You can pass sample weights argument to Random Forest fit method

sample_weight : array-like, shape = [n_samples] or None

Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.

In older version there were a preprocessing.balance_weights method to generate balance weights for given samples, such that classes become uniformly distributed. It is still there, in internal but still usable preprocessing._weights module, but is deprecated and will be removed in future versions. Don't know exact reasons for this.

Update

Some clarification, as you seems to be confused. sample_weight usage is straightforward, once you remember that its purpose is to balance target classes in training dataset. That is, if you have X as observations and y as classes (labels), then len(X) == len(y) == len(sample_wight), and each element of sample witght 1-d array represent weight for a corresponding (observation, label) pair. For your case, if 1 class is represented 5 times as 0 class is, and you balance classes distributions, you could use simple

sample_weight = np.array([5 if i == 0 else 1 for i in y])

assigning weight of 5 to all 0 instances and weight of 1 to all 1 instances. See link above for a bit more crafty balance_weights weights evaluation function.

Handle Imbalanced Classes In Random Forest, For your problem, suppose if 1 class is represented 5 times, as 0 class is, and you balance classes distributions, then simply use:. A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

This is really a shame that sklearn's "fit" method does not allow specifying a performance measure to be optimized. No one around seem to understand or question or be interested in what's actually going on when one calls fit method on data sample when solving a classification task.

We (users of the scikit learn package) are silently left with suggestion to indirectly use crossvalidated grid search with specific scoring method suitable for unbalanced datasets in hope to stumble upon a parameters/metaparameters set which produces appropriate AUC or F1 score.

But think about it: looks like "fit" method called under the hood each time always optimizes accuracy. So in end effect, if we aim to maximize F1 score, GridSearchCV gives us "model with best F1 from all modesl with best accuracy". Is that not silly? Would not it be better to directly optimize model's parameters for maximal F1 score? Remember old good Matlab ANNs package, where you can set desired performance metric to RMSE, MAE, and whatever you want given that gradient calculating algo is defined. Why is choosing of performance metric silently omitted from sklearn?

At least, why there is no simple option to assign class instances weights automatically to remedy unbalanced datasets issues? Why do we have to calculate wights manually? Besides, in many machine learning books/articles I saw authors praising sklearn's manual as awesome if not the best sources of information on topic. No, really? Why is unbalanced datasets problem (which is obviously of utter importance to data scientists) not even covered nowhere in the docs then? I address these questions to contributors of sklearn, should they read this. Or anyone knowing reasons for doing that welcome to comment and clear things out.

UPDATE

Since scikit-learn 0.17, there is class_weight='balanced' option which you can pass at least to some classifiers:

The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).

Bagging and Random Forest for Imbalanced Classification, Load libraries from sklearn.ensemble import RandomForestClassifier import numpy When using RandomForestClassifier a useful setting is  Unbalanced classification using Unbalanced classification using RandomForestClassifier in sklearn. 0 votes . Visit this Scikit Learn Tutorial to know more.

If the majority class is 1, and the minority class is 0, and they are in the ratio 5:1, the sample_weight array should be:

sample_weight = np.array([5 if i == 1 else 1 for i in y])

Note that you do not invert the ratios.This also applies to class_weights. The larger number is associated with the majority class.

Dealing with unbalanced classe, SVM, Random Forest and Decision , bagged decision trees on an imbalanced classification problem We can use the RandomForestClassifier class from scikit-learn and use a  Handle imbalanced classes in random forests in scikit-learn. Train Random Forest While Balancing Classes. When using RandomForestClassifier a useful setting is class_weight=balanced wherein classes are automatically weighted inversely proportional to how frequently they appear in the data.

Use the parameter class_weight='balanced'

From sklearn documentation: The balanced mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))

How to Handle Imbalanced Classes in Machine Learning, I am going to use the random forest classifier function in the scikit-learn library and the cross_val_score function (using the default scoring  This documentation is for scikit-learn version 0.11-git — Other versions. Citing. If you use the software, please consider citing scikit-learn. This page. 8.6.1. sklearn.ensemble.RandomForestClassifier

Hi instead of sample_weight,use class_weight=balanced in 'RandomForestClassifier' object

Important three techniques to improve machine learning model , For this guide, we'll use a synthetic dataset called Balance Scale Data, which you can or 0 (negative class) if the scale is not balanced: Transform into binary classification. Python 1. from sklearn.ensemble import RandomForestClassifier​  accuracy. We use metrics such as true negative rate, true positive rate, weighted accuracy, G-mean, precision, recall, and F-measure to evaluate the performance of learning algorithms on imbalanced data. These metrics have been widely used for comparison. All the metrics are functions of the confusion matrix as shown in Table 2.

3.2.4.3.1. sklearn.ensemble.RandomForestClassifier, The dataset has three classes and highly imbalanced. RF is a bagging type of ensemble classifier that uses many such single trees to make predictions. specified parameter values using scikit-sklearn implemented GridSearchCV. RandomForestClassifier(bootstrap=True, class_weight=class_weight, from sklearn.ensemble import RandomForestClassifier #Create a Gaussian Classifier clf=RandomForestClassifier(n_estimators=100) #Train the model using the training sets y_pred=clf.predict(X_test) clf.fit(X_train,y_train) # prediction on test set y_pred=clf.predict(X_test) #Import scikit-learn metrics module for accuracy calculation from sklearn

Fitting model on imbalanced datasets and how to fight bias , A random forest classifier. The sub-sample size is controlled with the max_samples parameter if Grow trees with max_leaf_nodes in best-first fashion. Scikit-learn is an open-source machine learning library for python. It provides a variety of regression, classification, and clustering algorithms. In my previous post, A Brief Tour of Sklearn, I discussed several methods for regression using the machine learning package. In this post, we will go over some of the basic methods for building

Class imbalance with weighted random forest, from sklearn.datasets import fetch_openml df, y = fetch_openml('adult', version=2 As a baseline, we could use a classifier which will always predict the majority class The RandomForestClassifier is as well affected by the class imbalanced,​  A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this example is to illustrate the nature of decision boundaries of different classifiers. This should be taken with a grain of salt, as the intuition conveyed by these examples does not necessarily carry over to real datasets.

Comments
  • But how would input every sample in the training set that is the minority class into the array-[n_samples]?
  • @mlo don't get meaning of your comment, can't you rephrase please
  • Sorry. What I meant was what exactly would you input for '[n_samples]'? Would that just be an array of all the labels in the data set? For example if you have X(features) and y(labels) would you just use the function like: fit(X, y, sample_weight = y). If you wouldn't mind could you provide an example, perhaps using my situation above where y = [1,1,0,0,0,0,0,0,0,0](the ratio is 5:1). How would I adjust the weights with: sample_weight = [n_samples]?
  • @mlo as it will be messy in comments, updated my answer with info on sample_weights usage. for y = [1,1,0,0,0,0,0,0,0,0] it can be sw = [1, 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
  • Thanks again. Since the parameter in sklearn takes array-like it came up with error when using list-like sample_weight = [5 if i == 0 else 1 for i in y] so just did sample_weight = np.array([5 if i == 0 else 1 for i in y]) and everything worked out fine
  • Hello, how your answer differs from already existing one? Just one line of code doesn't answer the question. Please, write more detailed answers.