Ich arbeite mit dem Dataset Boston housing
X, y = load_boston(return_X_y=True)
Hier ist mein code:
Code: Alles auswählen
# 5-fold cross-validation
k_folds = KFold(n_splits=5, random_state=10, shuffle=True)
# performance metrics
def mse_score(y_true, y_pred):
return np.mean( (y_true - y_pred) ** 2.0)
def rmse_score(y_true, y_pred):
return np.sqrt(mse_score(y_true, y_pred))
def mad_score(y_true, y_pred):
return np.mean( np.abs(y_true - y_pred) )
def pr2_score(y_true, y_pred):
return np.corrcoef(y_true, y_pred)[0, 1] ** 2.0
perf_metrics = {"MSE": mse_score,
"RMSE": rmse_score,
"Pseudo-R2": pr2_score,
"MAD": mad_score
}
# pre-instantiation
df_metrics = pd.DataFrame(index=[0], columns=["Fold", "Metric", "Train", "Test"])
# main loop
k, f = 0, 0
for (train, test) in k_folds.split(X):
f += 1
# separate variables and folds
x_train = X.values[train],
x_test = X.values[test]
y_train = y.values[train]
y_test = y.values[test]
# fit model
lr = LinearRegression().fit(x_train, y_train)
y_train_pred = ml.predict(x_train)
y_test_pred = ml.predict(x_test)
# compute metrics
for pf in list(perf_metrics.keys()):
df_metrics.loc[k, "Fold"] = f
df_metrics.loc[k, "Metric"] = pf
df_metrics.loc[k, "Train"] = perf_metrics[pf](y_train.ravel(),y_train_pred.ravel())
df_metrics.loc[k, "Test"] = perf_metrics[pf](y_test.ravel(), y_test_pred.ravel())
k += 1
# final organization
df_metrics = df_metrics.apply(pd.to_numeric, errors="ignore");ValueError Traceback (most recent call last)
<ipython-input-47-3aad90aea50c> in <module>
27
28 df_metrics.loc[k, "Train"] = perf_metrics[pf](y_train.ravel(),y_train_pred.ravel())
---> 29 df_metrics.loc[k, "Test"] = perf_metrics[pf](y_test.ravel(), y_test_pred.ravel())
30 k += 1
31
<ipython-input-8-0c6c11603f73> in mse_score(y_true, y_pred)
9 # homework: https://scikit-learn.org/stable/modules ... on-metrics
10 def mse_score(y_true, y_pred):
---> 11 return np.mean( (y_true - y_pred) ** 2.0) #instead of using the sklearn function mean_squared_error the function is defined
12
13 def rmse_score(y_true, y_pred):
ValueError: operands could not be broadcast together with shapes (102,) (101,)