Random forest feature names. expected_value[1],data=ord_test_t.
A random forest classifier will be fitted to compute the feature importances. index_feature_name_tuple is the list of tuples where the first element of each tuple is the index of the feature and the second one stands for the name of the feature. feature_importances_ important_names = feature_names [importances > np. Apr 5, 2022 · But this doesn't copy the feature values of the columns. Per my comment above, here is your example with the CustomFeautures() function removed: Jun 20, 2024 · Random Forest Cross-Valdidation for feature selection Description This function shows the cross-validated prediction performance of models with sequentially reduced number of predictors (ranked by variable importance) via a nested cross-validation procedure. A random forest regressor. We can use the Random Forest algorithm for feature importance implemented in scikit-learn as the RandomForestRegressor and RandomForestClassifier classes. Using the penguin data, let's build a classifier to predict the species ( Adelie, Gentoo, or Chinstrap) from the other 7 columns. inputCol= raw_data_col, \. feature_importances_, index=X. from sklearn import tree. rf = RandomForestClassifier () rf. feature_importances_ But I don't know what the "named_steps["step_name"]" are. We first create an instance of the Random Forest model, with the default parameters. 1. colors import ListedColormapfrom sklearn. DecisionTreeClassifier() or RandomForestClassifier() class. 3 version, is there anyway to take a look? First, you can access what was the best model by doing: best_estimator = gs_fit. Let’s quickly make a random forest with only the two most important variables, the max temperature 1 day prior and the historical average and see how the performance compares. Demystifying Feature Sampling This example shows the use of a forest of trees to evaluate the importance of features on an artificial classification task. I've managed to create a plot that shows the importances and uses the original variable names as labels but right now it's ordering the variable names in the order they were in the dataset (and not by order of May 6, 2018 · The feature importance ranks the most important feature for the entire model, "Delay Related DMS With Advice", in my case. Oct 11, 2021 · Feature selection in Python using Random Forest. columns # feature importances from random forest fit rf rank = rf. Bunch , try and create the df using df = pd. Or the varimp function in the cforest package. 3 version Describe the issue linked to the documentation I have loaded a vendor model and want to review the feature names of the RF built in sklearn == 0. I tried something like this: named_steps = X_train. # trees. These N observations will be sampled at random with replacement. sfm. so in your code # Train a RandomForest model. feature_importance = np. Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources. plot(kind='barh') Slightly more detailed answer with a full example: Assuming you trained your As an alternative, the permutation importances of rf are computed on a held out test set. Also, it can affect your model performance during training and inference. 6, 1:0. Aug 11, 2016 · 0. Feb 9, 2017 · # list of column names from original data cols = data. Sep 7, 2017 · Here is the code to run the random forest model: ## Import the random forest model. preprocessing import StandardScalerfrom 1. If ‘auto’ and data is pandas DataFrame Jul 10, 2015 · 6. sfm = SelectFromModel(rf, threshold=0. I use this code to generate a list of types that look like this: (feature_name, feature_importance). Say you have created a classifier: Jun 15, 2023 · In this guide - learn how to get feature importance from a Python's Scikit-Learn RandomForestRegressor or RandomForestClassifier, and how to plot and communicate the importance of features from the training set after fitting the model. It is also one of the most-used algorithms, due to its simplicity and diversity (it can be used for both classification and regression tasks). Here we can use Random Forest Classifier class as an estimator model. Dec 9, 2023 · The following Python code snippet demonstrates how to extract and visualize feature importance from a Random Forest Regressor using the Boston housing dataset from sklearn. Here we demonstrate the method with a two-dimensional data set plotted in the left figure below. Remember, decision trees are prone to overfitting. Then you can access this model's feature importances by doing. csv with 2 columns: the feature importance of a random forest model and the name of that feature. Obviously, you can chain these and directly do: plot_tree(rf_model. feature_importances_) If ‘auto’ and data is pandas DataFrame, data columns names are used. The variable name which has importance score > 0. expected_value[1],data=ord_test_t. 15. In addition, some other random forest functions can also be used here, e. Several techniques can be employed to calculate feature May 23, 2015 · Yes! Alternatively you can use the function vimp in the randomForestSRC package. . feature_importances_} df = pd. May 11, 2018 · X = Feature to be split on; Entropy(T,X) = The entropy calculated after the data is split on feature X; Random Forests. Mar 16, 2021 · By the looks of the input , boston is a sklearn. feature_names Jun 13, 2017 · Load the feature importances into a pandas series indexed by your column names, then use its plot method. I have included all the stages in a Pipeline. Apr 20, 2024 · Visualizing Classifier Trees. those in the right node. which we want to get named features for. array (importance) Jun 25, 2019 · This post aims to introduce how to obtain feature importance using random forest and visualize it in a different format. and I have problems showing the used features in my model. The number of trees in the forest. argsort(importances)[::-1], it doesn't have the feature names you want to appear as ticks on your X axis. Feature randomness, also known as feature bagging or “ the random subspace method ”(link resides outside ibm. For R, use importance=T in the Random Forest constructor then type=1 in R's importance () function. There is a module named variable_importance in arimo package which will give you the features selected by random forest classifier. The output is something like this: SparseVector(2, {0: 0. More data doesn’t always mean better quality. For this example, I’ll use the Boston dataset, which is a regression dataset. DataFrame(d) The feature importance data frame is something like below: And the model will have the method to transform and also feature importance. fit(train_X,train_y)# now fit our model for training data prediction=model. Jan 27, 2020 · Therefore, I need the attribute names. Mar 16, 2021 at 14:42. pyplot as pltfrom matplotlib. model_selection import train_test_splitfrom sklearn. It is perhaps the most popular and widely used machine learning algorithm given its good or excellent performance across a wide range of classification and regression predictive modeling problems. name: The name of the current step in the pipeline we are at. Series(model. Random forests (RF) construct many individual decision trees at training. Here it's an example but I cannot export to . columns. The blue bars are the feature importances of the forest, along with their inter-trees variability represented by the error bars. iloc[4],feature_names=ord_test_t. # this will remove the 5 worst features determined by their feature importance computed by the RF classifier. Then we'll define RFECV and fit it on training x and y data. The number will depend on the width of the dataset, the wider, the larger N can be. feature_selection. Then, we can use dtreeviz to display the tree and interrogate the model to learn more about how it makes decisions and to learn more about our data. featureImportances, but this does not give me feature/ column names, rather just the feature number. Supported criteria are “gini” for the Gini impurity and “entropy Dec 27, 2017 · Additionally, if we are using a different model, say a support vector machine, we could use the random forest feature importances as a kind of feature selection method. content_copy. It is a set of Decision Trees. , the coefficients of a linear model), the goal of recursive feature May 28, 2014 · As mentioned in the comments, it looks like the order or feature importances is the order of the "x" input variable (which I've converted from Pandas to a Python native data structure). Predictions are formed by aggregating predictions of individual trees Sep 8, 2021 · I want to retrieve label / class specific feature importances from a Random Forest or a Decision tree without training n_class times a one vs. What I get is below: The permutation importance is calculated on the training set to show how much the model relies on each feature during training. For example, give regressor_. columns But this doesn't work. SyntaxError: Unexpected token < in JSON at position 4. schema Jul 2, 2024 · Feature importance in Random Forest provides valuable insights into which features significantly impact the model’s predictions. Random Forests and Categorical Features. 21. 000 from the dataset (called N records). 4}) My question is how can I associate the name of the columns with the original name of the function? Feb 25, 2021 · Random Forest Logic. For a classifier model trained using X: feat_importances = pd. Decision Trees #. Mar 22, 2023 · I built a Random Forest classification model and when I try to view the decision trees in it by running this code: spacing=3, decimals=3, feature_names=X. Scikit learn - Plot forest importance. Feb 19, 2018 · 3. , the random forest importance criterion) or using a more general approach that is independent of the full model. rf = RandomForestClassifier() Aug 5, 2016 · def extract_feature_names(model, name) -> List[str]: """Extracts the feature names from arbitrary sklearn models. Oct 18, 2020 · The random forest model provided by the sklearn library has around 19 model parameters. com), generates a random subset of features, which ensures low train_data = np. mean (importances)] print important_names. score(X_test, y_test) As you can see Aug 11, 2023 · The code below allows me to plot all feature importances. columns) feat_importances. shape [ 1 ])] forest = RandomForestClassifier ( random_state = 0 ) forest . You need something like this, assuming df is your Pandas DataFrame The Forest Name Generator can generate thousands of ideas for your project, so feel free to keep clicking and at the end use the handy copy feature to export your forest names to a text editor of your choice. RFE #. import numpy as npimport pandas as pdimport seaborn as snsimport matplotlib. This is one of the 100+ free recipes of the IPython Cookbook, Second Edition, by Cyrille Rossant, a guide to numerical computing and data science in the Jupyter Notebook. array(iris. Jun 12, 2019 · Node splitting in a random forest model is based on a random subset of features for each tree. 22. criterion{“gini”, “entropy”}, default=”gini”. As a library I am using scikit-learn in Python. Returns: Jun 17, 2016 · indices is an array of indices returned from your np. indices = np. Feature ranking with recursive feature elimination. RFE. 3. So usually RF covers all columns from your dataset Random Forest is no exception. DataFrame(boston. feature_names, class_names=data. Mar 8, 2024 · Sadrach Pierre. Feature Importance in Random Forest. feature import StringIndexer, VectorAssembler. bestModel. here's some of my code. show() Above is the example of a single decision tree which is also known as estimator in Random forest, that makes you visualise how the plot_tree function will make you visualise your single decision tree. Apr 22, 2021 · I am trying to use Shapley summary_plot to inspect the top twenty features in my Random Forest classifier. May 28, 2024 · Answer: We pick random features in a Random Forest to increase diversity among the trees, reducing overfitting and improving model robustness. feature_importances_. Say there are M features or input variables. Explanation(values=shap_values[1])[4],base_values=explainer. The code begins by importing the necessary modules, loading the dataset, and then splitting it into features and the target variable. Moreover, Random Forest is rather fast, robust, and can show feature importances which can be quite useful. get_feature_names() should give the features in the order they arrive Apr 26, 2021 · Random forest is an ensemble machine learning algorithm. argsort(rank),cols)) # the dictionary key are the importance rank; the values are the feature name A Random Survival Forest ensures that individual trees are de-correlated by 1) building each tree on a different bootstrap sample of the original training data, and 2) at each node, only evaluate the split criterion for a randomly selected subset of features and thresholds. You can extract the feature names from the VectorAssembler object: %python. Dec 7, 2018 · Outlier detection with random forests. Clustering with random forests can avoid the need of feature transformation (e. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. fit ( X_train , y_train ) Dec 30, 2020 · If you’re writing a story, there’s a high probability that it will feature a forest somewhere in it. fit(X_train, y_train) The number of trees in the forest. nlargest(20). #. import matplotlib. And to be sure that the match between numeric value and variable name is correct. Apr 15, 2016 · Make sure your feature names are in a numpy array, not a python list. Jun 4, 2021 · From this Tutorial and Feature Importance I try to make my own random forest tree. ml. ensemble import RandomForestClassifier ## This line instantiates the model. The question here deals with extracting only feature importance: How to extract feature importances from an Sklearn pipeline Jun 16, 2021 · At the moment, StandardScaler doesn't support it; since xgboost is completely unaffected by feature scaling, I would suggest dropping it and replacing the numerical portion of the ColumnTransformer with "passthrough". tolist()) Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. Also accepts a string that specifies an attribute name/path for extracting feature importance (implemented with attrgetter). rf. coef_ in case of TransformedTargetRegressor or named_steps. Nipun Batra, R Yeeshu Dhurandhar. Each tree may contain sqrt(num_of_features) or log2(num_of_features) or whatever but these columns are selected at random. Changed in version 0. named_steps["preprocessing"]. ML. It only copies the shap values, expected_value and feature names. 0 is the feature selected by random forest classifier. A tree can be seen as a piecewise constant approximation. Jan 24, 2019 · 1. target_names, filled=True) plt. Jul 11, 2017 · now after the the fit I can get the random forest and the feature importance using cvModel. Args: model: The Sklearn model, transformer, clustering algorithm, etc. categorical_feature (list of str or int, or 'auto', optional (default='auto')) – Categorical features. pyplot as plt. For one, it's really hard generate enough "folds" to get a meaningful validation number while still having enough data to build Oct 16, 2022 · We'll load the dataset and take feature and label parts of iris data. data,columns=boston. Let’s first import all the objects we need, that are our dataset, the Random Forest regressor and the object that will perform the RFE with CV. Using the cv values from randomforest is a valid approach to feature selection, if you've selected sensible settings for your oob sampling. DataFrame(data. 15) # Train the selector. fit ( X_train, y_train) Powered By. Oct 17, 2018 · Is there a function which allows to get the list of names of columns used during the training of the Random Forest Regressor model? RF uses all features from your dataset. trim_5_df = DataFrame(columns=features_to_use) run=1. feature_names) – anky. iris = load_iris() x = iris. As the scikit-learn implementation of RandomForestClassifier uses a random subsets of n features features at each split, it is able to dilute the dominance Mar 29, 2020 · Random Forest Feature Importance. Could somebody explain me what named_steps["step_name"] is? Mar 10, 2017 · 回帰問題でも分類問題と同様のやり方で"Feature Importances"が得られました．"Boston" データセットでは，"RM", "LSTAT" のfeatureが重要との結果です．（今回は，「特徴量重要度を求める」という主旨につき，ハイパーパラメータの調整は，ほとんど行っていませんので注意願います．） Next we are going to cast the feature importance and feature names as Numpy arrays. Also, Random Forest limits the greatest disadvantage of Decision Trees. We then fit this to our training data. The random forest algorithm can be described as follows: Say the number of observations is N. max_depth: The number of splits that each decision tree is allowed to make. data = load_breast_cancer() df = pd. Parameters. Medium: Day (3) — DS — How to use Seaborn for Categorical Plots. # empty dataframe. The importance calculations can be model based (e. Feb 7, 2023 · A Random Forest Algorithm actually extends the Bagging Algorithm (if bootstrapping = true) because it partially leverages the bagging to form uncorrelated decision trees. This can be used for arimo package in Mar 20, 2019 · I'm wondering how I can extract feature importances from a Random Forest in scikit-learn with the feature names when using the classifier in a pipeline with preprocessing. Jan 27, 2017 · I am trying to plot feature importances for a random forest model and map each feature importance back to the original coefficient. This is my code, I've tidied it up a bit to make it relevant to your task: features_to_use = fea_cols # this is a list of features. featureImportances) preds = model. Usually, we measure the loss that would be done if we lose the true values of that feature. ensemble import RandomForestClassifier feature_names = [ f 'feature { i } ' for i in range ( X . Feature Randomness — In a normal decision tree, when it is time to split a node, we consider every possible feature and pick the one that produces the most separation between the observations in the left node vs. shap. The ebook and printed book are available for purchase at Packt Publishing. A random forest is a meta estimator that fits a number of decision tree regressors on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. argsort(importances) plt. Then rs_clf. Jan 14, 2024 · Random Forest Feature Importance. DataFrame(shap_values, columns = feature_names). It is also easy to use given that it has few key hyperparameters and sensible heuristics for configuring […] Aug 1, 2018 · The tree_to_json is the raw rules which should be transferred to rules with the feature names. 2 and Pyspark. values to see an array of your column names. g. clf. Cite. best_estimator_. target RFECV requires estimator model. feature_importances_ in case of Pipeline with its last step named clf . utils. To get reliable results in Python, use permutation importance, provided here and in our rfpimp package (via pip ). from pyspark. Nov 15, 2022 · [Question] - Is there any way to view feature names of Random Forest Model built in scikit learn ==0. This shows that the low cardinality categorical feature, sex and pclass are the most important feature. Reference. named_steps["step_name"]. If the issue persists, it's likely a problem on our side. Jan 12, 2017 · If you stored your feature names as a numpy array and made sure it is consistent with the features passed to the model, you can take advantage of numpy indexing to do it. By leveraging methods like Mean Decrease in Impurity, Permutation Importance, and SHAP values, you can enhance your understanding, improve model performance, and make informed decisions in feature selection and Nov 20, 2019 · Várias árvores de decisão = RANDOM FOREST. So I found this code: rf_gridsearch. # features that have an importance of more than 0. For the purpose of finding a cool forest name, we created this enchanted forest name generator: Random Forest Name The random forest algorithm is an extension of the bagging method as it utilizes both bagging and feature randomness to create an uncorrelated forest of decision trees. data, columns=data. feature_names) # transformed list to array feature_names[support] array(['sepal width (cm)', 'petal width (cm)'], dtype='|S17') EDIT. Ranking property gives us ranking position of a each feature. Random Forests can handle categorical features without extensive preprocessing like one-hot encoding. We pass both the features and the target variable, so the model can learn. Scikit learn - Ensemble methods. fit(X_train, y_train) ## And score it on your testing data. feature_importances_ # form dictionary of feature ranks and features features_dict = dict(zip(np. However even if bootstrapping = false, Random Forests go one step extra to really make sure the trees are not correlated — feature sampling. Author. This is returning the Random Forest that yielded the best results. I am trying to plot the feature importances of random forest classifier with with column names. criterion{“gini”, “entropy”, “log_loss”}, default=”gini”. , categorical features). – David Meu. This technique introduces variety in the trees that comprise the forest, Nov 16, 2023 · The following are the basic steps involved when executing the random forest algorithm: Pick a number of random records, it can be any number, such as 4, 20, 76, 150, or even 2. predict(test_X) Oct 20, 2016 · A good suggestion by wrwrwr! Since the order of the feature importance values in the classifier's 'feature_importances_' property matches the order of the feature names in 'feature. Random forest uses many trees, and thus, the variance is reduced; Random forest allows far more exploration of feature combinations as well; Decision trees gives Variable Importance and it is more if there is reduction in impurity (reduction in Gini impurity) Each tree has a different Order of Importance Oct 8, 2023 · The other cool feature of Random Forest is that we could use it to reduce the number of features for any tabular data. waterfall_plot(shap. rf = RandomForestClassifier() ## Fit the model on your training data. To extract the relevant feature information from the pipeline with the tree model, you must extract the correct pipeline stage. First, I preprocessed my data with MaxAbsScaler and OneHotEncoder using make_pipeline and Dec 6, 2017 · Inorder to identify and select most important features, # Create a selector object that will use the random forest classifier to identify. You want to pull a single DecisionTreeClassifier out of your forest. How can I show the top N feature importances ? %matplotlib inline. It will give a pandas dataframe with variable name, importance score. May 23, 2020 · A decision tree is the basic unit of a random forest, and chances are you already know what it is (just perhaps not by that name). columns, clf. The most important of these parameters which we need to tweak, while hyperparameter tuning, are: n_estimators: The number of decision trees in the random forest. Let’s look at how the Random Forest is constructed. stages[-2]. Ou seja, basicamente, o algoritmo possui 4 passos: Seleção aleatória de algumas features; 2. The Random Forest algorithm has built-in feature importance which can be computed in two ways: Gini importance (or mean decrease impurity), which is computed from the Random Forest structure. some algorithms like decision trees offer importance scores) or by using a statistical method. Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The scikit-learn Random Forest feature importance and R's default Random Forest feature importance strategies are biased. best_features = best_estimator. estimators_[0], feature_names=data. Random Forests, a popular ensemble learning technique, are known for their efficiency and interpretability. columns)) I get this error: ValueError: feature_names must contain 155 elements, got 19. So, I tried the below. From the documentation, base_estimator_ is a DecisionTreeClassifier and estimators_ is a list of DecisionTreeClassifier. This allows us to construct a two column data frame from the two arrays. This are the columns in my prediction data: Aug 8, 2020 · Currently, I'm working on Random Forest for classification. Jul 5, 2016 · Hi I would like to create a . RFE(estimator, *, n_features_to_select=None, step=1, verbose=0, importance_getter='auto') [source] #. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both Jan 1, 2021 · shap_values have (num_rows, num_features) shape; if you want to convert it to dataframe, you should pass the list of feature names to the columns parameter: rf_resultX = pd. One feature at a time the values are scrambled and the loss in predictive accuracy is measured. Type in boston. If list of int, interpreted as indices. transform(testData) May 16, 2022 · Typically models in SparkML are fit as the last stage of the pipeline. I am using Spark 2. data y = iris. Refresh. importances = rf. Forest = RandomForestClassifier(n_estimators = 100, compute_importances=True) # Fit the training data to the training output and create the decision. Published. 22: The default value of n_estimators changed from 10 to 100 in 0. Nov 21, 2015 · Random Forest can measure the relative importance of any feature in a classification task. Aug 31, 2023 · 2. I interpret it as that, this variable should be important either in Class 0 or Class 1 but from the output I get, it is unimportant in both Classes. Seleção da feature mais adequada para a posição de Oct 18, 2019 · From this question pyspark-mllib-random-forest-feature-importances I see there is a method called featureImportances that return a SparseVector. The plot on the left shows the Gini importance of the model. rf = RandomForestClassifier(labelCol="labels", featuresCol="features", numTrees=36) model = rf. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. 10. zip(x. After being fit, the model provides a feature_importances_ property that can be accessed to retrieve the relative importance scores for each input feature. , probability and interpretation. columns,'FI':my_entire_pipe[2]. array(train_data) # Create the random forest object which will include all the parameters. def plot_feature_importance (importance,names,model_type): #Create arrays from feature importance and feature names. Rather than just calling it a forest or the dark forest, give your forest a real mystical name that engages your readers. fit(trainingData) #print(rf. You can just simply make a barplot with the variable importance values to display the strength of the importance of the variables. If list of str, interpreted as feature names (need to specify feature_name as well). The input X is sentences and i am using tfidf (HashingTF + IDF) + StringIndexer to generate the feature vectors. You can get index_feature_name_tuple using the following script: df_fitted. import numpy as np feature_names = np. It works well “out-of-the-box” with no hyperparameter tuning and way better than linear algorithms which makes it a good option. A number m, where m < M, will be selected at random at each node from the total number of features, M. The models are an instance of either the tree. Predictions from all trees are pooled to make the final prediction; the mode of the classes for classification or the mean prediction for regression. Jan 5, 2022 · A random forest classifier is what’s known as an ensemble algorithm. csv correclty If the issue persists, it's likely a problem on our side. Now that the theory is clear, let’s apply it in Python using sklearn. varImpPlot makes this automatically. January 14, 2024. features = bvsa_train_feature. n_estimatorsint, default=100. d = {'Stats':X. However, you can remove this problem by simply planting more trees! Apr 5, 2024 · Feature Importance in Random Forests. Step-by-step data science - Random Forest Classifier. ensemble import RandomForestClassifier. Feb 11, 2019 · feature_importances_ in Scikit-Learn is based on that logic, but in the case of Random Forest, we are talking about averaging the decrease in impurity over trees. The function to measure the quality of a split. Unexpected token < in JSON at position 4. However, with only 70 observations you're not going to get great results. Pros: fast calculation; easy to retrieve — one command; Cons: biased approach, as it has a tendency to inflate the importance of continuous features or high-cardinality categorical Features are scored either using the provided machine learning model (e. rest model. Further, it is also helpful to sort the features, and select the top N features to show. For regression tasks, the mean or average prediction Mar 29, 2020 · The feature importance of the Random Forest classifier is saved inside the model itself, so all I need to do is to extract it and combine it with the raw feature names. # for the fit. Instead, they work well with both one-hot encoded and label-encoded categorical variables: For one-hot encoded features: Random Forests can naturally handle binary features (0 or 1) without issues. importances = best_rf. They work by building numerous decision trees during training, and the final prediction is the average of the individual tree predictions. columns', you can use the zip() function. For classification tasks, the output of the random forest is the class selected by most trees. You can quickly fit a Random Forest and define a list of meaningful columns in your data. class sklearn. from sklearn. figure(figsize=(10,100)) Jun 29, 2020 · Built-in Random Forest Importance. Indeed, permuting the values of these features will lead to most decrease in accuracy score of the model on the test set. Random forest is a flexible, easy-to-use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. Make sure to set compute_importances=True. Given an external estimator that assigns weights to features (e. The reason for this is that it leverages multiple instances of another algorithm at the same time to find a result. A decision tree is a method model decisions or classifications Using a random forest to select important features for regression. In Random Forest, the selection of random features for each decision tree is a fundamental strategy to enhance the model's performance. The change to your code is: from sklearn. But I want feature names as well. model=RandomForestClassifier(n_estimators=estimator, max_depth=depth, max_features=feature)# a simple random forest model model. Explore and run machine learning code with Kaggle Notebooks | Using data from Income classification. keyboard_arrow_up. tn oa sl eu dx wo gn hu qx jo
