1. Advisor
  2. ml
  3. ml code examples
View all ml analysis

How to use ml - 10 common examples

To help you get started, we’ve selected a few ml examples, based on popular ways it is used in public projects.

Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.

github repetere / modelscript / build / modelscript.esm.js View on Github external
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.SL.GaussianNB = GaussianNB;
github repetere / modelscript / build / modelscript.esm.js View on Github external
}
}

/* 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.SL.GaussianNB = GaussianNB;
MachineLearning.SL.LogisticRegression = LogisticRegression;
github repetere / modelscript / build / modelscript.esm.js View on Github external
}
    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;
github repetere / modelscript / build / modelscript.esm.js View on Github external
});
    }
    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;
github repetere / modelscript / build / modelscript.esm.js View on Github external
/* 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.SL.GaussianNB = GaussianNB;
MachineLearning.SL.LogisticRegression = LogisticRegression;
MachineLearning.SL.DecisionTreeClassifier = DecisionTreeClassifier;
MachineLearning.SL.RandomForestClassifier = RandomForestClassifier;
github repetere / modelscript / build / modelscript.esm.js View on Github external
}

/* 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.SL.GaussianNB = GaussianNB;
MachineLearning.SL.LogisticRegression = LogisticRegression;
MachineLearning.SL.DecisionTreeClassifier = DecisionTreeClassifier;
github repetere / modelscript / build / modelscript.esm.js View on Github external
/* 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.SL.GaussianNB = GaussianNB;
MachineLearning.SL.LogisticRegression = LogisticRegression;
MachineLearning.SL.DecisionTreeClassifier = DecisionTreeClassifier;
MachineLearning.SL.RandomForestClassifier = RandomForestClassifier;

MachineLearning.Stat.PCA = PCA;
github repetere / modelscript / build / modelscript.esm.js View on Github external
/* 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.SL.GaussianNB = GaussianNB;
MachineLearning.SL.LogisticRegression = LogisticRegression;
MachineLearning.SL.DecisionTreeClassifier = DecisionTreeClassifier;
MachineLearning.SL.RandomForestClassifier = RandomForestClassifier;
github repetere / modelscript / build / modelscript.esm.js View on Github external
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',
  descale: 'descaleOptions',
  label: 'labelOptions',
  labelEncoder: 'labelOptions',
github repetere / modelscript / build / modelscript.esm.js View on Github external
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',
  descale: 'descaleOptions',
  label: 'labelOptions',
  labelEncoder: 'labelOptions',
  labeldecode: 'labelOptions',

ml

Machine learning tools

GitHub
MIT
Latest version published 1 month ago

Package Health Score

84 / 100
Full package analysis

Popular ml functions

Similar packages