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() → {Array.<float>}

Standard error of estimated coefficients and intercept.
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Type
Array.<float>

devianceResiduals() → {Array.<float>}

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

explainedVariance() → {float}

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Type
float

featuresCol() → {string}

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Returns:
Type
string

labelCol() → {string}

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Returns:
Type
string

meanAbsoluteError() → {float}

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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float

meanSquaredError() → {float}

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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float

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

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module:eclairjs/ml/regression.LinearRegressionModel

numInstances() → {integer}

Number of instances in DataFrame predictions
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Type
integer

objectiveHistory() → {Array.<float>}

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Returns:
Type
Array.<float>

predictionCol() → {string}

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Type
string

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

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module:eclairjs/sql.DataFrame

pValues() → {Array.<float>}

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

r2() → {float}

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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float

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

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

rootMeanSquaredError() → {float}

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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float

totalIterations() → {integer}

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Returns:
Type
integer

tValues() → {Array.<float>}

T-statistic of estimated coefficients and intercept.
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Returns:
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Array.<float>