Package: VectorForgeML 0.1.0
Musheer Mohd
VectorForgeML: High-Performance Machine Learning Framework with C++ Acceleration
Machine learning utilities for fast vectorized model training. Methods are based on standard statistical learning references such as Hastie et al. (2009) <doi:10.1007/978-0-387-84858-7>.
Authors:
VectorForgeML_0.1.0.tar.gz
VectorForgeML_0.1.0.tar.gz(r-4.7-arm64)VectorForgeML_0.1.0.tar.gz(r-4.7-x86_64)VectorForgeML_0.1.0.tar.gz(r-4.6-arm64)VectorForgeML_0.1.0.tar.gz(r-4.6-x86_64)
VectorForgeML_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
VectorForgeML/json (API)
| # Install 'VectorForgeML' in R: |
| install.packages('VectorForgeML', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mohd-musheer/vectorforgeml/issues
Last updated from:69a3e326b3. Checks:6 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 130 | ||
| linux-devel-x86_64 | OK | 130 | ||
| source / vignettes | OK | 191 | ||
| linux-release-arm64 | OK | 127 | ||
| linux-release-x86_64 | OK | 129 | ||
| wasm-release | OK | 115 |
Exports:accuracy_scoreColumnTransformerconfusion_matrixconfusion_statsDecisionTreedrop_constant_columnsf1_scorefind_best_kfit_linear_modelKMeansKNNLabelEncoderLinearRegressionLogisticRegressionmacro_f1macro_precisionmacro_recallMinMaxScalermseOneHotEncoderPCAPipelineplot_confusion_matrixprecision_scorepredict_linear_modelr2_scoreRandomForestrecall_scoreRidgeRegressionrmseSoftmaxRegressionStandardScalertrain_test_split
Dependencies:Rcpp
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Accuracy Score | accuracy_score |
| Column Transformer | ColumnTransformer ColumnTransformer-class |
| Confusion Matrix | confusion_matrix |
| Confusion Matrix Statistics | confusion_stats |
| Decision Tree Model | DecisionTree DecisionTree-class |
| Drop Constant Columns | drop_constant_columns |
| F1 Score | f1_score |
| Find Best K | find_best_k |
| Fit Linear Model (Fast C++ backend) | fit_linear_model |
| KMeans Clustering | KMeans KMeans-class |
| K-Nearest Neighbors Model | KNN KNN-class |
| Label Encoder | LabelEncoder LabelEncoder-class |
| Linear Regression Model | LinearRegression LinearRegression-class |
| Logistic Regression Model | LogisticRegression LogisticRegression-class |
| Macro Precision | macro_f1 |
| Macro Precision | macro_precision |
| Macro Precision | macro_recall |
| Standard Scaler | MinMaxScaler MinMaxScaler-class |
| Mean Squared Error | mse |
| One Hot Encoder | OneHotEncoder OneHotEncoder-class |
| Principal Component Analysis | PCA PCA-class |
| Pipeline | Pipeline Pipeline-class |
| Plot Confusion Matrix | plot_confusion_matrix |
| Precision Score | precision_score |
| Predict Linear Model | predict_linear_model |
| R2 Score | r2_score |
| Random Forest Model | RandomForest RandomForest-class |
| Recall Score | recall_score |
| Ridge Regression Model | RidgeRegression RidgeRegression-class |
| Root Mean Squared Error | rmse |
| Softmax Regression Model | SoftmaxRegression SoftmaxRegression-class |
| Drop Constant Columns | StandardScaler StandardScaler-class |
| Train Test Split | train_test_split |
