This README file explains the Python code that showcases the comparison of different machine learning models using the Digits dataset. The code demonstrates how to use various classifiers and regression models, evaluates their performance using k-fold cross-validation, and compares their accuracy scores.
The code imports necessary libraries and datasets, prepares the data, and evaluates the performance of three machine learning models using k-fold cross-validation.
The Digits dataset is loaded using the load_digits() function from the sklearn.datasets module. This dataset contains handwritten digit images, where each image is represented as an 8x8 matrix of pixel values.
Three different machine learning models are used for classification:
- Logistic Regression (LogReg): A linear model used for binary and multiclass classification.
- Support Vector Classifier (SVC): A classifier that constructs hyperplanes to separate classes.
- Random Forest Regressor (RF): An ensemble learning method that combines multiple decision trees. k-Fold Cross-Validation The KFold class from sklearn.model_selection is used to perform k-fold cross-validation. The dataset is split into k subsets (folds), and the model is trained and evaluated k times, with each fold serving as the validation set once.
The code iterates through the k folds, training and testing each model using the current fold's data. The accuracy scores of each model are collected and stored in separate lists (socre_l, socre_svc, and socre_rf).
The accuracy scores for each model are printed out at the end of the code. The results show the accuracy achieved by each model on different folds of the dataset.
Additionally, a simpler way to perform cross-validation using the cross_val_score function from sklearn.model_selection is mentioned. This function directly returns the cross-validation scores without the need for explicit iteration.
This README provides an overview of the Python code's purpose, including data loading, model comparison, k-fold cross-validation, and result interpretation. By evaluating different models on the Digits dataset, users can gain insights into which models perform better for this specific task.