feat: add ml-cart to the package (#93)

Author: targos

README.md

@@ -31,6 +31,7 @@ It will be available as the global `ML` variable. The package is in UMD format a
 * Partial least squares (PLS): [`ML.PLS`](https://github.com/mljs/pls)
 * Cross-validation: [`ML.CrossValidation`](https://github.com/mljs/cross-validation)
 * Confusion matrix: [`ML.ConfusionMatrix`](https://github.com/mljs/confusion-matrix)
+* Decision tree classifier: [`ML.DecisionTreeClassifier`](https://github.com/mljs/decision-tree-cart)
 
 ### Artificial neural networks (ANN)
 
@@ -46,6 +47,7 @@ It will be available as the global `ML` variable. The package is in UMD format a
 * Exponential regression: [`ML.ExponentialRegression`](https://github.com/mljs/regression-exponential)
 * Theil-Sen regression: [`ML.TheilSenRegression`](https://github.com/mljs/regression-theil-sen)
 * Robust polynomial regression: [`ML.RobustPolynomialRegression`](https://github.com/mljs/regression-robust-polynomial)
+* Decision tree regression: [`ML.DecisionTreeRegression`](https://github.com/mljs/decision-tree-cart)
 
 ### Optimization
 

package-lock.json

@@ -35,6 +35,7 @@
     "binary-search": "^1.3.2",
     "ml-array-utils": "^0.4.0",
     "ml-bit-array": "^1.0.0",
+    "ml-cart": "^1.0.8",
     "ml-confusion-matrix": "^0.3.0",
     "ml-cross-validation": "^1.1.0",
     "ml-distance": "^2.1.1",

package.json

@@ -1,5 +1,7 @@
 'use strict';
 
+const mlCart = require('ml-cart');
+
 const ML = exports;
 
 // Try to keep this list in the same structure as the README.
@@ -16,6 +18,7 @@ ML.KNN = require('ml-knn');
 ML.PLS = require('ml-pls');
 ML.CrossValidation = require('ml-cross-validation');
 ML.ConfusionMatrix = require('ml-confusion-matrix');
+ML.DecisionTreeClassifier = mlCart.DecisionTreeClassifier;
 
 // Artificial neural networks
 ML.FNN = require('ml-fnn');
@@ -30,6 +33,7 @@ ML.PowerRegression = Regression.PowerRegression;
 ML.ExponentialRegression = Regression.ExponentialRegression;
 ML.TheilSenRegression = Regression.TheilSenRegression;
 ML.RobustPolynomialRegression = Regression.RobustPolynomialRegression;
+ML.DesisionTreeRegression = mlCart.DesisionTreeRegression;
 
 // Optimization
 ML.levenbergMarquardt = require('ml-levenberg-marquardt');