Computes R-squared coefficient of determination.
R2 = 1 - sum((y_hat - y_test)**2) / sum((y_test - np.mean(y_test))**2)
Parameters : | yhat : 1d array or list of floats – estimated values of y y : 1d array or list of floats – true values |
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Examples
r2 = computeR2(yhat, y)
Extracts the 2D Pareto-optimal front from a 2D numpy array.
Parameters : | data : numpy ndarray, or pandas.DataFrame
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Examples
p = getParetoFront(data)
Calculates the normalized mean-squared error.
Parameters : | yhat : 1d array or list of floats
y : 1d array or list of floats
min_y, max_y : float, float
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Examples
nmse = nmse(yhat, y)
Partitions data into training and test sets. Assumes the last column of data is y.
Parameters : | data : numpy ndarray, or pandas.DataFrame
threshold : float
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Examples
TODO
Facilitates standardizing data by subtracting the mean and dividing by the standard deviation. Set reverse to True to perform the inverse operation.
Parameters : | X : numpy ndarray, or pandas.DataFrame
scaleDict: dict, default None :
reverse: boolean, default False :
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Examples
TODO