dtda_ml

DTDA ML allows you to run machine learning models like KNN, Linear Regression, Logistic Regression, SVM

4 models are currently available:

KNN
Linear Regression
Logistic Regression
SVM

=== MLTools features ===

Use MLTools.new() to create a new MLTools. _dropVariable() and _getVariable() allows you to drop a column, or keep column from an array. This is usefull to create X_train and Y_train for all models

Example:

data = [ [1, 1, 1, 0, 1], [1, 1, 1, 1, 1], [1, 0, 0, 0, 0] ]
var ml = MLTools.new()
var X_train = ml._dropVariable(data, data[0].size()-1) #return an array of array without the last column
var y_train = ml._getVariable(data, data[0].size()-1) #return an array of array only with the last column

=== KNN Model ===

Use DTDAKNN.new() to create a new model. _fit() and _predict() allows you to train and use the model. This model is better for classification.

Example:

var knn = DTDAKNN.new(3)
knn._fit(X_train, y_train)
var X_test = [ [1, 1, 0, 1] ]
print("KNN prediction: ", knn._predict(X_test))

=== Linear Regression Model ===

Use DTDALinReg.new() to create a new model. _fit() and _predict() allows you to train and use the model. This model is better for Regression.

Example:

var linreg = DTDALinReg.new(0.01, 1000)
linreg._fit(X_train, y_train)
var X_test = [ [1, 1, 0, 1] ]
print("Linear Regression prediction: ", linreg._predict(X_test))

=== Logistic Regression Model ===

Use DTDALogReg.new() to create a new model. _fit() and _predict() allows you to train and use the model. This model is only for classification (1 or 0).

Example:

var logreg = DTDALogReg.new(0.01, 1000)
logreg._fit(X_train, y_train)
var X_test = [ [1, 1, 0, 1] ]
print("Logistic Regression prediction: ", logreg._predict(X_test))

=== SVM Model ===

Use DTDASVM.new() to create a new model. _fit() and _predict() allows you to train and use the model. This model is only for classification (1 or -1).

Example:

var svm = DTDASVM.new(0.01, 0.01, 1000)
svm._fit(X_train, y_train)
var X_test = [ [1, 1, 0, 1] ]
print("SVM prediction: ", svm._predict(X_test))

DTDA ML - Machine Learning models

dtda_ml