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 25 views

Eli5 scikit-learn

The function accepts a text and an integer n, returning the n most frequently occurring words in the text. It uses the Eli5 library for feature extraction and visualization.

The type of code 2024-12-16 12:14:57 11 views

CountVectorizer train_test_split

This code implements a text classification model using CountVectorizer for text vectorization, MultinomialNB for classification, and PermutationImportance from Eli5 to display feature importance.

The type of code 2024-12-16 12:10:01 4 views

eli5 sklearn

This function uses Eli5's PermutationImportance to analyze the distribution of word lengths in a text and selects the top n most frequent word lengths.

The type of code 2024-12-16 12:08:40 3 views

scikit-learn CountVectorizer

This function uses CountVectorizer to convert texts into a matrix of token counts and then uses MultinomialNB for sentiment classification, calculating the accuracy on the test set.

Machine learning 2024-12-16 12:04:04 3 views

eli5 sklearn.feature_extraction.text.CountVectorizer

This function uses the eli5 library to display the feature importance from a CountVectorizer. It first creates a pipeline with a CountVectorizer and eli5.show_weights, then fits the pipeline to the text data, retrieves the feature importance, and formats the output using a table formatter.

Function 2024-12-07 16:29:06 29 views

scikit-learn (CountVectorizer

This function converts texts into a matrix of token counts using CountVectorizer and then uses Multinomial Naive Bayes classifier to classify sentiments.

Machine learning classifier 2024-12-07 16:28:24 3 views

Scikit-learn CountVectorizer

This function uses CountVectorizer to convert text data into a document-term matrix, then uses MultinomialNB for classification, and calculates the accuracy on the test set.

Machine learning classification 2024-12-07 16:27:12 3 views

eli5 sklearn.feature_extraction.text.CountVectorizer

The code defines a function named random_eli5_function that randomly selects a function from the eli5 library to explain a given text. First, it randomly selects a function from the eli5 library, then randomly selects a text as input. Then, it creates a CountVectorizer object to convert the text into a feature matrix. Next, it uses the selected eli5 function to explain the model, and then formats the explanation using a table.

Function 2024-12-07 16:16:20 5 views

eli5 sklearn

This function uses the eli5 library to explain a random machine learning model. It first uses CountVectorizer for text vectorization, then uses eli5's SKLearnExplainer to explain the model's predictions.

Machine Learning Model Explanation 2024-12-07 16:05:19 6 views