scikit-learn LogisticRegression

This function uses the LogisticRegression model from the scikit-learn library to train a dataset and evaluate its accuracy on a test set.

Machine learning 2024-12-16 12:17:51 30 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

Scikit-learn StandardScaler

This function uses Scikit-learn library for feature scaling with StandardScaler and classification prediction with LogisticRegression. It first splits the dataset into training and test sets, trains the model on the training set, and then makes predictions on the test set.

Machine learning classification 2024-12-16 12:06:45 7 views

Packages and

This function uses the SHAP library to explain the predictions of a random forest regression model. It first splits the data into training and testing sets, then trains the model, and uses SHAP to generate explanations for each test instance.

The type of code 2024-12-16 12:04:23 5 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

NumPy scikit-learn

This function first applies PCA for dimensionality reduction and then uses a Random Forest classifier for classification, calculating the accuracy on the test set.

Machine learning classification function 2024-12-16 12:03:59 4 views

CatBoost Numpy

This function uses CatBoost's CatBoostClassifier to initialize a random state, then shuffles the indices based on this random state, and finally splits the data into training and testing sets according to the specified training ratio.

Data segmentation function 2024-12-16 12:03:39 3 views

Scikit-learn; Random

This function uses the Random Forest Classifier to classify data. It first splits the dataset into training and testing sets, then trains the model, makes predictions on the test set, and finally calculates and returns the model's accuracy.

The type of code 2024-12-16 12:03:20 3 views

Pandas Scikit-learn

This function uses a logistic regression model to predict outcomes on a given dataset. It first splits the dataset into training and testing sets using the train_test_split function. Then, it creates a logistic regression model and trains it using the training set. Finally, it uses the trained model to make predictions on the test set and returns these predictions.

Machine learning 2024-12-16 12:02:55 9 views

scikit-learn RandomForestClassifier

This function uses the RandomForestClassifier from the scikit-learn library to classify the iris dataset and calculate the accuracy on the test set.

Machine learning classifier 2024-12-16 12:02:52 3 views