af66764dca05e5b26048533eb2e71fbbb705cbf0,treeinterpreter/treeinterpreter.py,,_predict_forest,#Any#Any#Any#,117

Before Change

            predictions.append(pred)
        
        
        return (np.mean(predictions, axis=0), np.mean(biases, axis=0),
            np.mean(contributions, axis=0))


def predict(model, X, joint_contribution=False):
     Returns a triple (prediction, bias, feature_contributions), such

After Change

    else:
        mean_pred = None
        mean_bias = None
        mean_contribution = None

        for i, tree in enumerate(model.estimators_):
            pred, bias, contribution = _predict_tree(tree, X)

            if i < 1: // first iteration
                mean_bias = bias
                mean_contribution = contribution
                mean_pred = pred
            else:
                mean_bias = _iterative_mean(i, mean_bias, bias)
                mean_contribution = _iterative_mean(i, mean_contribution, contribution)
                mean_pred = _iterative_mean(i, mean_pred, pred)

        return mean_pred, mean_bias, mean_contribution


def predict(model, X, joint_contribution=False):
     Returns a triple (prediction, bias, feature_contributions), such

Instances