code python run

X_val = [all_X, ka, A_X, C_X, L_X, M_X, P_X]
X_val_test = [X_t, ka_t, A_X_t, C_X_t, L_X_t, M_X_t, P_X_t]

for i in range(7):
model = LR.fit(X_val[i],y)
pred = model.predict(X_val_test[i])
if (train_cnt == 1):
R_square.append(abs(metrics.r2_score(y_t,model.predict(X_val_test[i]))))
RMSE.append(sqrt(mean_squared_error(y_t,pred)))
MAE.append(mean_absolute_error(y_t,pred))
Cor, _ = pearsonr(y_t,pred)
CORR.append(abs(Cor))
else :
R_square[i] += abs(metrics.r2_score(y_t,model.predict(X_val_test[i])))
RMSE[i] += sqrt(mean_squared_error(y_t,pred))
MAE[i] += mean_absolute_error(y_t,pred)
Cor, _ = pearsonr(y_t,pred)
CORR[i] += abs(Cor)

for i in range(7):
R_square[i] = (R_square[i]/k)
RMSE[i] = RMSE[i]/k
MAE[i] = MAE[i]/k
CORR[i] = CORR[i]/k

i want to change this so i take the highest not the average