Scikit-learn This function uses the RandomForestClassifier from Scikit-learn to fit a model on the training data, make predictions on the test data, and then calculate and return the accuracy of the predictions. Machine learning classification 2024-12-16 12:17:59 29 views
The code This code defines a function named `visualize_shap_values` that visualizes SHAP values for a given dataset and model. It first creates a SHAP explainer, then computes SHAP values, and visualizes these values using the `waterfall` method from the SHAP library. In the main function `main`, a random dataset is generated, a simple linear regression model is created, and the `visualize_shap_values` function is called to visualize the SHAP values of the model on the data. The type of code 2024-12-16 12:17:40 39 views
Scikit-learn This function uses the Random Forest algorithm to train the given training set and perform classification predictions on the test set, finally returning the accuracy of the model. Machine learning classification 2024-12-16 12:17:21 17 views
CatBoost This function uses a CatBoost model to predict the output based on the given features and returns the prediction. Function 2024-12-16 12:17:21 13 views
Scikit-learn NumPy This function generates a random dataset, splits it into training and test sets, trains a random forest classifier, makes predictions on the test set, and calculates the accuracy of the predictions. Function 2024-12-16 12:17:13 18 views
scikit-learn This function trains a Random Forest classifier on the training data, predicts labels on the test data, and returns the accuracy of the predictions. Machine learning classification 2024-12-16 12:17:01 19 views
Allennlp Predictor This function randomly selects a predictor from the Allennlp library and uses a randomly generated text for prediction. Function 2024-12-16 12:16:42 19 views
SHAP library This function uses the SHAP library to analyze the impact of each feature on the predictions of a given model. It visualizes the SHAP values for a subset of test data points. The type of code 2024-12-16 12:16:41 16 views
Allennlp Vocabulary This function uses the Allennlp library to predict the label of a given text. It first loads a pre-trained model and vocabulary. Then, it splits the input text into words and finds the index of each word in the vocabulary. Next, it creates an instance and passes it to the model for prediction. Finally, it returns the predicted label. The type of code 2024-12-16 12:16:22 18 views
CatBoost NumPy This function uses a CatBoost model to predict the output for a given data. It first checks if the input model is an instance of CatBoostClassifier, and then makes predictions using the model. Function 2024-12-16 12:16:06 13 views
