eli5 scikit-learn

This function creates a randomly chosen machine learning pipeline using CountVectorizer for text vectorization and LogisticRegression or DecisionTreeClassifier as the classifier. Then, it uses the show_weights function from the eli5 library to generate a feature importances table and formats it as an HTML table.

Machine learning 2024-12-16 12:16:48 24 views

The code

This code defines a function that uses the SHAP library to analyze the interpretability of a machine learning model. It accepts a trained model and input data, computes SHAP values, and visualizes them using waterfall plots.

The type of code 2024-12-16 12:16:05 13 views

The code

The code uses the LightGBM library to extract feature importance from a trained model. It also uses NumPy for numerical operations.

The type of code 2024-12-16 12:15:48 10 views

Packages and

PermutationImportance, Explain_weights, Explainer, FeatureImportances

The type of code 2024-12-16 12:14:38 10 views

SHAP library

This function uses the SHAP library to generate a feature importance plot for a specified feature. It first creates a SHAP explainer, then computes the SHAP values for the feature, and displays these values using a waterfall plot.

The type of code 2024-12-16 12:14:02 10 views

LightGBM NumPy

This function trains a LightGBM model for binary classification using the provided training data and a set of parameters such as the number of leaves, maximum depth, learning rate, and number of estimators.

Machine learning 2024-12-16 12:13:07 5 views

Scikit-learn machine

This function uses a random forest classifier to classify the given data and returns the accuracy of the classification.

The type of code 2024-12-16 12:11:38 3 views

XGBoost NumPy

This function generates a machine learning model using the XGBoost library by randomly selecting parameters such as learning rate, max depth, min child weight, and subsample ratio. The function accepts feature matrix X and label vector y as inputs and returns the trained XGBoost model.

Function 2024-12-16 12:05:07 7 views

eli5 sklearn

This function randomly selects a method from the Eli5 library to explain machine learning models, such as feature importance or interpretive predictions. Here, a random forest model and the Iris dataset are used as examples.

The type of code 2024-12-16 12:04:13 4 views

The code

This code defines a function that uses the LightGBM library to train and predict a binary classification model and calculates the accuracy on the test set. The function includes steps such as data generation, model training, parameter setting, model prediction, and accuracy calculation.

The type of code 2024-12-16 12:01:50 4 views