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

Keras MNIST

This code defines a function that uses the Keras library to classify the MNIST dataset. It first loads the dataset, then preprocesses the data, then constructs a simple neural network model, compiles and trains the model, and finally evaluates the model performance.

The type of code 2024-12-16 12:17:47 31 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

Matplotlib NumPy

This function plots the 2D density estimate of a given dataset using Gaussian kernel density estimation.

Function 2024-12-16 12:17:14 14 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

PyTorch torchvision

This function is used to load and transform data from a specified PyTorch dataset. It supports the CIFAR10 and MNIST datasets. The function returns a DataLoader object for batch data loading.

Data Loading and Conversion Functions 2024-12-16 12:17:06 27 views

Eli5 scikit-learn

This function uses the PermutationImportance from the Eli5 library to calculate the feature importance for a given dataset. It does so by shuffling the feature values and retraining the model, then comparing the change in model performance to evaluate the importance of each feature.

Machine learning 2024-12-16 12:16:57 28 views

numpy sklearn.decomposition.PCA

This function first applies PCA (Principal Component Analysis) to reduce the dimensions of the dataset, and then uses PermutationImportance to explain the importance of the principal components. PermutationImportance evaluates the importance of features by randomly shuffling feature values and observing the change in model performance.

The type of code 2024-12-16 12:16:44 14 views

NLTK Tokenization

This code defines a function that randomly selects a specific number of stopwords from the NLTK stopwords dataset.

The type of code 2024-12-16 12:16:00 19 views

Bokeh ColumnDataSource

This function creates a scatter plot and uses a color bar to represent the color values of the data. It accepts a dataset containing x, y, and color values.

Bokeh visualization 2024-12-16 12:15:56 9 views