scikit-learn numpy

This function uses PCA (Principal Component Analysis) to reduce the dimensionality of the data. First, it standardizes the data using StandardScaler, and then applies PCA to select the specified number of principal components.

Function 2024-12-16 12:14:21 6 views

pandas numpy

This function takes a pandas DataFrame as input, normalizes the numeric columns using StandardScaler from sklearn, and returns the original DataFrame with the normalized numeric columns concatenated.

Function 2024-12-16 12:13:22 7 views

Vaex Numpy

This function first generates a random dataset using the Vaex library, then splits it into training and testing sets. It then standardizes the features using StandardScaler and trains a linear regression model.

Custom function 2024-12-16 12:10:48 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

scikit-learn numpy

This function uses PCA (Principal Component Analysis) to reduce the dimensionality of the dataset. It first standardizes the input data using StandardScaler, then applies PCA to select a specified number of principal components.

Python Function 2024-12-16 11:49:51 3 views

Scikit-learn Numpy

This function uses the Scikit-learn library's SVC (Support Vector Classifier) to train and evaluate a classification model. It first splits the dataset into training and test sets, then scales the features, creates an SVC model with a linear kernel, trains the model, makes predictions on the test set, and finally returns the classification report.

Machine learning classifier 2024-12-16 11:47:55 7 views

Scikit-learn train_test_split

The code uses Scikit-learn's `train_test_split` for dataset splitting, `StandardScaler` for feature standardization, `SVC` for the Support Vector Classifier with a radial basis function (RBF) kernel, and `classification_report` for evaluation.

The type of code 2024-12-16 11:41:11 3 views

scikit-learn eli5

This function first uses StandardScaler to standardize the features, then uses PCA for dimensionality reduction, and finally uses PermutationImportance to evaluate the importance of the features.

Machine learning 2024-12-16 11:39:17 4 views

Eli5 Numpy

This function uses PermutationImportance from the Eli5 library to select the most important features from a dataset. It first standardizes the features, then applies PCA for dimensionality reduction, and finally calculates the importance scores of the features using PermutationImportance and selects the top few features with the highest scores.

The type of code 2024-12-16 11:39:11 8 views

Numpy Scikit-learn

This function applies PCA (Principal Component Analysis) to the input data, then uses PermutationImportance to assess the importance of each feature in the reduced dataset. Eli5 library is used to generate explanations for feature importance.

Machine learning 2024-12-16 11:39:11 4 views