How to use the ml.default.Regression function in ml
To help you get started, we’ve selected a few ml examples, based on popular ways it is used in public projects.
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repetere / modelscript / src / ml.js View on Github 
import { default as MachineLearning, } from 'ml';
import { RandomForestRegression, RandomForestClassifier, } from 'ml-random-forest';
import { default as LogisticRegression, } from 'ml-logistic-regression';
import { DecisionTreeRegression, DecisionTreeClassifier, } from 'ml-cart';
import { GaussianNB, } from 'ml-naivebayes';
import { default as MultivariateLinearRegression, } from 'ml-regression-multivariate-linear';
import { default as PCA, } from 'ml-pca';
import { ReinforcedLearningBase, UpperConfidenceBound, ThompsonSampling, } from './ReinforcedLearning';
MachineLearning.Regression = Object.assign({},
MachineLearning.Regression);
MachineLearning.SL = Object.assign({},
MachineLearning.SL);
MachineLearning.Stat = Object.assign({},
MachineLearning.Stat);
MachineLearning.RL = Object.assign({},
MachineLearning.RL, {
ReinforcedLearningBase,
UpperConfidenceBound,
ThompsonSampling,
});
MachineLearning.UpperConfidenceBound = UpperConfidenceBound;
MachineLearning.ThompsonSampling = ThompsonSampling;
MachineLearning.Regression.DecisionTreeRegression = DecisionTreeRegression;
MachineLearning.Regression.RandomForestRegression = RandomForestRegression;
MachineLearning.Regression.MultivariateLinearRegression = MultivariateLinearRegression;import { default as MachineLearning, } from 'ml';
import { RandomForestRegression, RandomForestClassifier, } from 'ml-random-forest';
import { default as LogisticRegression, } from 'ml-logistic-regression';
import { DecisionTreeRegression, DecisionTreeClassifier, } from 'ml-cart';
import { GaussianNB, } from 'ml-naivebayes';
import { default as MultivariateLinearRegression, } from 'ml-regression-multivariate-linear';
import { default as PCA, } from 'ml-pca';
import { ReinforcedLearningBase, UpperConfidenceBound, ThompsonSampling, } from './ReinforcedLearning';
MachineLearning.Regression = Object.assign({},
MachineLearning.Regression);
MachineLearning.SL = Object.assign({},
MachineLearning.SL);
MachineLearning.Stat = Object.assign({},
MachineLearning.Stat);
MachineLearning.RL = Object.assign({},
MachineLearning.RL, {
ReinforcedLearningBase,
UpperConfidenceBound,
ThompsonSampling,
});
MachineLearning.UpperConfidenceBound = UpperConfidenceBound;
MachineLearning.ThompsonSampling = ThompsonSampling;
MachineLearning.Regression.DecisionTreeRegression = DecisionTreeRegression;
MachineLearning.Regression.RandomForestRegression = RandomForestRegression;
MachineLearning.Regression.MultivariateLinearRegression = MultivariateLinearRegression;MachineLearning.Regression = Object.assign({},
MachineLearning.Regression);
MachineLearning.SL = Object.assign({},
MachineLearning.SL);
MachineLearning.Stat = Object.assign({},
MachineLearning.Stat);
MachineLearning.RL = Object.assign({},
MachineLearning.RL, {
ReinforcedLearningBase,
UpperConfidenceBound,
ThompsonSampling,
});
MachineLearning.UpperConfidenceBound = UpperConfidenceBound;
MachineLearning.ThompsonSampling = ThompsonSampling;
MachineLearning.Regression.DecisionTreeRegression = DecisionTreeRegression;
MachineLearning.Regression.RandomForestRegression = RandomForestRegression;
MachineLearning.Regression.MultivariateLinearRegression = MultivariateLinearRegression;
MachineLearning.SL.GaussianNB = GaussianNB;
MachineLearning.SL.LogisticRegression = LogisticRegression;
MachineLearning.SL.DecisionTreeClassifier = DecisionTreeClassifier;
MachineLearning.SL.RandomForestClassifier = RandomForestClassifier;
MachineLearning.Stat.PCA = PCA;
/**
* @namespace
* @see {@link https://github.com/mljs/ml}
*/
export const ml = MachineLearning;MachineLearning.Regression);
MachineLearning.SL = Object.assign({},
MachineLearning.SL);
MachineLearning.Stat = Object.assign({},
MachineLearning.Stat);
MachineLearning.RL = Object.assign({},
MachineLearning.RL, {
ReinforcedLearningBase,
UpperConfidenceBound,
ThompsonSampling,
});
MachineLearning.UpperConfidenceBound = UpperConfidenceBound;
MachineLearning.ThompsonSampling = ThompsonSampling;
MachineLearning.Regression.DecisionTreeRegression = DecisionTreeRegression;
MachineLearning.Regression.RandomForestRegression = RandomForestRegression;
MachineLearning.Regression.MultivariateLinearRegression = MultivariateLinearRegression;
MachineLearning.SL.GaussianNB = GaussianNB;
MachineLearning.SL.LogisticRegression = LogisticRegression;
MachineLearning.SL.DecisionTreeClassifier = DecisionTreeClassifier;
MachineLearning.SL.RandomForestClassifier = RandomForestClassifier;
MachineLearning.Stat.PCA = PCA;
/**
* @namespace
* @see {@link https://github.com/mljs/ml}
*/
export const ml = MachineLearning;MachineLearning.Regression = Object.assign({},
MachineLearning.Regression);
MachineLearning.SL = Object.assign({},
MachineLearning.SL);
MachineLearning.Stat = Object.assign({},
MachineLearning.Stat);
MachineLearning.RL = Object.assign({},
MachineLearning.RL, {
ReinforcedLearningBase,
UpperConfidenceBound,
ThompsonSampling,
});
MachineLearning.UpperConfidenceBound = UpperConfidenceBound;
MachineLearning.ThompsonSampling = ThompsonSampling;
MachineLearning.Regression.DecisionTreeRegression = DecisionTreeRegression;
MachineLearning.Regression.RandomForestRegression = RandomForestRegression;
MachineLearning.Regression.MultivariateLinearRegression = MultivariateLinearRegression;
MachineLearning.SL.GaussianNB = GaussianNB;
MachineLearning.SL.LogisticRegression = LogisticRegression;
MachineLearning.SL.DecisionTreeClassifier = DecisionTreeClassifier;
MachineLearning.SL.RandomForestClassifier = RandomForestClassifier;
MachineLearning.Stat.PCA = PCA;
/**
* @namespace
* @see {@link https://github.com/mljs/ml}
*/
export const ml = MachineLearning;
