How to use the ml.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 / build / modelscript.esm.js View on Github 
}
return this;
}
}
/* fix for rollup */
/* istanbul ignore next */
const mlf = (mlfModule__default) ? mlfModule__default : mlfModule;
const mlc = (mlcModule__default) ? mlcModule__default : mlcModule;
const mln = (mlnModule__default) ? mlnModule__default : mlnModule;
const { RandomForestRegression, RandomForestClassifier, } = mlf;
const { DecisionTreeRegression, DecisionTreeClassifier, } = mlc;
const { GaussianNB, } = mln;
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;});
}
return this;
}
}
/* fix for rollup */
/* istanbul ignore next */
const mlf = (mlfModule__default) ? mlfModule__default : mlfModule;
const mlc = (mlcModule__default) ? mlcModule__default : mlcModule;
const mln = (mlnModule__default) ? mlnModule__default : mlnModule;
const { RandomForestRegression, RandomForestClassifier, } = mlf;
const { DecisionTreeRegression, DecisionTreeClassifier, } = mlc;
const { GaussianNB, } = mln;
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);
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}
*/
const ml = MachineLearning;
const transformConfigMap = {
scale: 'scaleOptions',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}
*/
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}
*/
const ml = MachineLearning;
const transformConfigMap = {
