Class: LinearRegressionTrainingSummary

eclairjs/ml/regression.LinearRegressionTrainingSummary

Linear regression training results. Currently, the training summary ignores the training coefficients except for the objective trace.

Constructor

new LinearRegressionTrainingSummary()

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Extends

Methods

coefficientStandardErrors() → {Promise.<Array.<number>>}

Standard error of estimated coefficients and intercept.
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Promise.<Array.<number>>

devianceResiduals() → {Promise.<Array.<number>>}

The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
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Promise.<Array.<number>>

explainedVariance() → {Promise.<number>}

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Promise.<number>

featuresCol() → {Promise.<string>}

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Promise.<string>

labelCol() → {Promise.<string>}

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Promise.<string>

meanAbsoluteError() → {Promise.<number>}

Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss. Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
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Promise.<number>

meanSquaredError() → {Promise.<number>}

Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss. Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
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Promise.<number>

model() → {module:eclairjs/ml/regression.LinearRegressionModel}

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module:eclairjs/ml/regression.LinearRegressionModel

numInstances() → {Promise.<number>}

Number of instances in DataFrame predictions
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Promise.<number>

objectiveHistory() → {Promise.<Array.<number>>}

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Returns:
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Promise.<Array.<number>>

predictionCol() → {Promise.<string>}

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Promise.<string>

predictions() → {module:eclairjs/sql.DataFrame}

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module:eclairjs/sql.DataFrame

pValues() → {Promise.<Array.<number>>}

Two-sided p-value of estimated coefficients and intercept.
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Promise.<Array.<number>>

r2() → {Promise.<number>}

Returns R^2^, the coefficient of determination. Reference: http://en.wikipedia.org/wiki/Coefficient_of_determination Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
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Promise.<number>

residuals() → {module:eclairjs/sql.DataFrame}

Residuals (label - predicted value)
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module:eclairjs/sql.DataFrame

rootMeanSquaredError() → {Promise.<number>}

Returns the root mean squared error, which is defined as the square root of the mean squared error. Note: This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
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Promise.<number>

totalIterations() → {Promise.<number>}

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Promise.<number>

tValues() → {Promise.<Array.<number>>}

T-statistic of estimated coefficients and intercept.
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Promise.<Array.<number>>