def explained_variance(ypred,y):
    """
    Computes fraction of variance that ypred explains about y.
    Returns 1 - Var[y-ypred] / Var[y]

    interpretation:
        ev=0  =>  might as well have predicted zero
        ev=1  =>  perfect prediction
        ev<0  =>  worse than just predicting zero

    """
    assert y.ndim == 1 and ypred.ndim == 1
    vary = np.var(y)
    return np.nan if vary==0 else 1 - np.var(y-ypred)/vary
def explained_variance(ypred,y):
    """
    Computes fraction of variance that ypred explains about y.
    Returns 1 - Var[y-ypred] / Var[y]

    interpretation:
        ev=0  =>  might as well have predicted zero
        ev=1  =>  perfect prediction
        ev<0  =>  worse than just predicting zero

    """
    assert y.ndim == 1 and ypred.ndim == 1
    vary = np.var(y)
    return np.nan if vary==0 else 1 - np.var(y-ypred)/vary
def explained_variance ( ypred , y ) : assert y . ndim == 1 and ypred . ndim == 1 vary = np . var ( y ) return np . nan if vary == 0 else 1 - np . var ( y - ypred ) / vary
Computes fraction of variance that ypred explains about y.
    Returns 1 - Var[y-ypred] / Var[y]

    interpretation:
        ev=0  =>  might as well have predicted zero
        ev=1  =>  perfect prediction
        ev<0  =>  worse than just predicting zero
Computes fraction of variance that ypred explains about y . Returns 1 - Var [ y - ypred ] / Var [ y ]