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VectorForgeML (version 0.1.0)

High-Performance Machine Learning Framework with C++ Acceleration

Description

Machine learning utilities for fast vectorized model training. Methods are based on standard statistical learning references such as Hastie et al. (2009) .

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Version

Install

install.packages('VectorForgeML')

Version

0.1.0

License

Apache License (>= 2)

Maintainer

Musheer Mohd

Last Published

February 28th, 2026

Functions in VectorForgeML (0.1.0)

RidgeRegression-class

Ridge Regression Model
SoftmaxRegression-class

Softmax Regression Model
confusion_matrix

Confusion Matrix
StandardScaler-class

Drop Constant Columns
drop_constant_columns

Drop Constant Columns
f1_score

F1 Score
confusion_stats

Confusion Matrix Statistics
find_best_k

Find Best K
RandomForest-class

Random Forest Model
macro_precision

Macro Precision
macro_recall

Macro Precision
VectorForgeML-package

VectorForgeML: High-Performance ML Framework
mse

Mean Squared Error
predict_linear_model

Predict Linear Model
precision_score

Precision Score
plot_confusion_matrix

Plot Confusion Matrix
fit_linear_model

Fit Linear Model (Fast C++ backend)
rmse

Root Mean Squared Error
train_test_split

Train Test Split
macro_f1

Macro Precision
recall_score

Recall Score
r2_score

R2 Score
KNN-class

K-Nearest Neighbors Model
OneHotEncoder-class

One Hot Encoder
KMeans-class

KMeans Clustering
PCA-class

Principal Component Analysis
DecisionTree-class

Decision Tree Model
ColumnTransformer-class

Column Transformer
MinMaxScaler-class

Standard Scaler
LogisticRegression-class

Logistic Regression Model
Pipeline-class

Pipeline
accuracy_score

Accuracy Score
LinearRegression-class

Linear Regression Model
LabelEncoder-class

Label Encoder