It only takes a minute to sign up. I'm using the bigrf R-package to analyse a dataset with ca. After growing a forest of trees, I investigate the importance and relationship of the features in the relation to the 2 classes using, respectively, the fastimp and interactions functions, which produce very nice results.
However, I'm now interested in investigating the problem using 3 or more rather than 2 classes. In this case, the Gini variable importance calculated by fastimp only relates to overall importance.
My question is: Is there a way to calculate a class-specific Gini variable importance, or some similar measure? I assume visually the top feature will be more abundant in one group comparing with the other groups. Rank the features in each combination and eventually plot the result and see if gini scores for feature x is higher on both combinations. Sign up to join this community.
The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Random Forest: Class specific feature importance Ask Question. Asked 5 years, 2 months ago. Active 1 year, 5 months ago. Viewed times. Misconstruction Misconstruction 4 4 bronze badges.
Please let me know if you find the solution. Active Oldest Votes. I can always update and add into answers. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. Upcoming Events. Featured on Meta. Responding to the Lavender Letter and commitments moving forward.
I am resigning as a moderator. Related 3.
Furthermore, the impurity-based feature importance of random forests suffers from being computed on statistics derived from the training dataset: the importances can be high even for features that are not predictive of the target variable, as long as the model has the capacity to use them to overfit.
This example shows how to use Permutation Importances as an alternative that can mitigate those limitations. The following shows how to apply separate preprocessing on numerical and categorical features.
We further include two random variables that are not correlated in any way with the target variable survived :. Prior to inspecting the feature importances, it is important to check that the model predictive performance is high enough. Indeed there would be little interest of inspecting the important features of a non-predictive model.
Here one can observe that the train accuracy is very high the forest model has enough capacity to completely memorize the training set but it can still generalize well enough to the test set thanks to the built-in bagging of random forests. The impurity-based feature importance ranks the numerical features to be the most important features. As an alternative, the permutation importances of rf are computed on a held out test set.
This shows that the low cardinality categorical feature, sex is the most important feature. It is also possible to compute the permutation importances on the training set. The difference between those two plots is a confirmation that the RF model has enough capacity to use that random numerical feature to overfit.
Total running time of the script: 0 minutes 3. Gallery generated by Sphinx-Gallery. Toggle Menu.
Subscribe to RSS
Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am working with RandomForestRegressor in python and I want to create a chart that will illustrate the ranking of feature importance. This is the code I used:. Load the feature importances into a pandas series indexed by your column names, then use its plot method. A barplot would be more than useful in order to visualize the importance of the features.
This code from spies dont work : plt. The method you are trying to apply is using built-in feature importance of Random Forest. This method can sometimes prefer numerical features over categorical and can prefer high cardinality categorical features. Please see this article for details. There are two other methods to get feature importance but also with their pros and cons. In scikit-learn from version 0. It is model agnostic. It can even work with algorithms from other packages if they follow the scikit-learn interface.
The complete code example:. The permutation-based importance can be computationally expensive and can omit highly correlated features as important. Feature Importance can be computed with Shapley values you need shap package. Computing SHAP values can be computationally expensive. The full example of 3 methods to compute Random Forest feature importance can be found in this blog post of mine. Learn more. Asked 3 years, 4 months ago.
Active 11 days ago. Viewed 66k times. This is the code I used: from sklearn. Any help solving this issue so I can create this chart will be greatly appreciated. Active Oldest Votes. Here is an example using the iris data set. Tan Duong 11 11 silver badges 23 23 bronze badges.
Series model. How did you make the colors? On my plot all bars are blue. Use this example using Iris Dataset : from sklearn. The y-ticks are not correct. To fix it, it should be plt.The feature importance variable importance describes which features are relevant. It can help with better understanding of the solved problem and sometimes lead to model improvements by employing the feature selection. In this post, I will present 3 ways with code examples how to compute feature importance for the Random Forest algorithm from scikit-learn package in Python.
I will show how to compute feature importance for the Random Forest with scikit-learn package and Boston dataset house price regression task. The permutation based importance can be used to overcome drawbacks of default feature importance computed with mean impurity decrease. As arguments it requires trained model can be any model compatible with scikit-learn API and validation test data. The features which impact the performance the most are the most important one.
The permutation based importance is computationally expensive. The permutation based method can have problem with highly-correlated features, it can report them as unimportant.
It is using the Shapley values from game theory to estimate the how does each feature contribute to the prediction. It can be easily installed pip install shap and used with scikit-learn Random Forest:. The computing feature importances with SHAP can be computationally expensive. However, it can provide more information like decision plots or dependence plots.
The 3 ways to compute the feature importance for the scikit-learn Random Forest were presented:. In my opinion, it is always good to check all methods, and compare the results. Maybe you will find interesting article about the Random Forest Regressor and when does it fail and why? Random Forest Built-in Feature Importance The Random Forest algorithm has built-in feature importance which can be computed in two ways: Gini importance or mean decrease impuritywhich is computed from the Random Forest structure.
It is a set of Decision Trees.
Each Decision Tree is a set of internal nodes and leaves. In the internal node, the selected feature is used to make decision how to divide the data set into two separate sets with similars responses within. The features for internal nodes are selected with some criterion, which for classification tasks can be gini impurity or infomation gain, and for regression is variance reduction.
We can measure how each feature decrease the impurity of the split the feature with highest decrease is selected for internal node. For each feature we can collect how on average it decreases the impurity. The average over all trees in the forest is the measure of the feature importance.
Explaining Feature Importance by example of a Random Forest
This method is available in scikit-learn implementation of the Random Forest for both classifier and regressor. It is worth to mention, that in this method we should look at relative values of the computed importances.
This biggest advantage of this method is a speed of computation - all needed values are computed during the Radom Forest training.
The drawbacks of the method is to tendency to prefer select as important numerical features and categorical features with high cardinality.Feature importance refers to techniques that assign a score to input features based on how useful they are at predicting a target variable. There are many types and sources of feature importance scores, although popular examples include statistical correlation scores, coefficients calculated as part of linear models, decision trees, and permutation importance scores.
Feature importance scores play an important role in a predictive modeling project, including providing insight into the data, insight into the model, and the basis for dimensionality reduction and feature selection that can improve the efficiency and effectiveness of a predictive model on the problem. Kick-start your project with my new book Data Preparation for Machine Learningincluding step-by-step tutorials and the Python source code files for all examples.
Feature importance refers to a class of techniques for assigning scores to input features to a predictive model that indicates the relative importance of each feature when making a prediction.
Feature importance scores can be calculated for problems that involve predicting a numerical value, called regression, and those problems that involve predicting a class label, called classification. The scores are useful and can be used in a range of situations in a predictive modeling problem, such as:.
Feature importance scores can provide insight into the dataset. The relative scores can highlight which features may be most relevant to the target, and the converse, which features are the least relevant. This may be interpreted by a domain expert and could be used as the basis for gathering more or different data. Feature importance scores can provide insight into the model.
Most importance scores are calculated by a predictive model that has been fit on the dataset. Inspecting the importance score provides insight into that specific model and which features are the most important and least important to the model when making a prediction. This is a type of model interpretation that can be performed for those models that support it.
Feature importance can be used to improve a predictive model. This can be achieved by using the importance scores to select those features to delete lowest scores or those features to keep highest scores. This is a type of feature selection and can simplify the problem that is being modeled, speed up the modeling process deleting features is called dimensionality reductionand in some cases, improve the performance of the model.
Often, we desire to quantify the strength of the relationship between the predictors and the outcome. Feature importance scores can be fed to a wrapper model, such as the SelectFromModel class, to perform feature selection. There are many ways to calculate feature importance scores and many models that can be used for this purpose.
Perhaps the simplest way is to calculate simple coefficient statistics between each feature and the target variable. For more on this approach, see the tutorial:. In this tutorial, we will look at three main types of more advanced feature importance; they are:. This is important because some of the models we will explore in this tutorial require a modern version of the library.
Running the example will print the version of the library. At the time of writing, this is about version 0. Each test problem has five important and five unimportant features, and it may be interesting to see which methods are consistent at finding or differentiating the features based on their importance. The dataset will have 1, examples, with 10 input features, five of which will be informative and the remaining five will be redundant.
We will fix the random number seed to ensure we get the same examples each time the code is run. Running the example creates the dataset and confirms the expected number of samples and features. Like the classification dataset, the regression dataset will have 1, examples, with 10 input features, five of which will be informative and the remaining five that will be redundant. Linear machine learning algorithms fit a model where the prediction is the weighted sum of the input values.
Examples include linear regression, logistic regression, and extensions that add regularization, such as ridge regression and the elastic net. All of these algorithms find a set of coefficients to use in the weighted sum in order to make a prediction.In many business cases it is equally important to not only have an accurate, but also an interpretable model. Knowing feature importance indicated by machine learning models can benefit you in multiple ways, for example:.
That is why in this article I would like to explore different approaches to interpreting feature importance by the example of a Random Forest model.
Most of them are also applicable to different models, starting from linear regression and ending with black-boxes such as XGBoost. One thing to note is that the more accurate our model is, the more we can trust feature importance measures and other interpretations. I assume that the model we build is reasonably accurate as each data scientist will strive to have such a model and in this article, I focus on the importance measures. For this example, I will use the Boston house prices dataset so a regression problem.
But the approaches described in this article work just as well with classification problems, the only difference is the metric used for evaluation. The only non-standard thing in preparing the data is the addition of a random column to the dataset. Below I inspect the relationship between the random feature and the target variable.
Random Forest Feature Importance Computed in 3 Ways with Python
As it can be observed, there is no pattern on the scatterplot and the correlation is almost 0. One thing to note here is that there is not much sense in interpreting the correlation for CHASas it is a binary variable and different methods should be used for it. I train a plain Random Forest model to have a benchmark. Briefly, on the subject of out-of-bag error, each tree in the Random Forest is trained on a different dataset, sampled with replacement from the original data.
This is similar to evaluating the model on a validation set. You can read more here. Well, there is some overfitting in the model, as it performs much worse on OOB sample and worse on the validation set. By overall feature importances I mean the ones derived at the model level, i. In decision trees, every node is a condition of how to split values in a single feature, so that similar values of the dependent variable end up in the same set after the split. So when training a tree we can compute how much each feature contributes to decreasing the weighted impurity.
It seems that the top 3 most important features are:. What seems surprising though is that a column of random values turned out to be more important than:. Intuitively this feature should have zero importance on the target variable. This approach directly measures feature importance by observing how random re-shuffling thus preserving the distribution of the variable of each predictor influences model performance.
The approach can be described in the following steps:. As for the second problem with this method, I have already plotted the correlation matrix above. I found two libraries with this functionality, not that it is difficult to code it. One thing to note about this library is that we have to provide a metric as a function of the form metric model, X, y.Often in data science we have hundreds or even millions of features and we want a way to create a model that only includes the most important features.
This has three benefits. First, we make our model more simple to interpret. Second, we can reduce the variance of the model, and therefore overfitting. Finally, we can reduce the computational cost and time of training a model.
Random Forests are often used for feature selection in a data science workflow. The reason is because the tree-based strategies used by random forests naturally ranks by how well they improve the purity of the node. This mean decrease in impurity over all trees called gini impurity. Nodes with the greatest decrease in impurity happen at the start of the trees, while notes with the least decrease in impurity occur at the end of trees.
Thus, by pruning trees below a particular node, we can create a subset of the most important features. Note: There are other definitions of importance, however in this tutorial we limit our discussion to gini importance.
The dataset used in this tutorial is the famous iris dataset. The Iris target data contains 50 samples from three species of Iris, y and four feature variables, X.
The scores above are the importance scores for each variable. There are two things to note. Second, Petal Length and Petal Width are far more important than the other two features. Clearly these are the most importance features. As can be seen by the accuracy scores, our original model which contained all four features is Thus, for a small cost in accuracy we halved the number of features in the model.
Preliminaries import numpy as np from sklearn. View the features X [ 0 : 5 ].Machine learning - Random forests
View the target data y. Create a selector object that will use the random forest classifier to identify features that have an importance of more than 0. Transform the data to create a new dataset containing only the most important features Note: We have to apply the transform to both the training X and test X data.