def runMLPRegressor(self): lm = MLPRegressor(hidden_layer_sizes=(250, ), activation='tanh', solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999) print("MLPRegressor\n") reg = lm.fit(self.m_X_train, self.m_y_train) predictY = lm.predict(self.m_X_test) score = lm.score(self.m_X_test, self.m_y_test) predictTraingY = lm.predict(self.m_X_train) lm.coef_ = lm.coefs_ lm.fit_intercept = lm.intercepts_ self.displayPredictPlot(predictY) self.displayResidualPlot(predictY, predictTraingY) self.dispalyModelResult(lm, predictY, score)