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While I was trying to rune the below code - creating a timseries split in order to maintain the temporal nature of my data & following the github issues #380 .. I encountered this issue -Please suggest if there is anything I should modify
-Code
tscv = TimeSeriesSplit(n_splits=5)
#Create train and test indices for each split
folds = []
splits = []
for train_index, test_index in tscv.split(x_train):
folds.append(train_index.tolist())
splits.append(test_index.tolist())
#Combine train/test indices into a list of tuples
custom_cv = {'validation_type': 'custom', 'folds': folds, 'splits': splits}
#Define AutoML with custom cross-validation
automl = AutoML(mode='Compete', total_time_limit=600, validation_strategy=custom_cv)
#Fit the model
automl.fit(x_train, y_train)
The text was updated successfully, but these errors were encountered:
While I was trying to rune the below code - creating a timseries split in order to maintain the temporal nature of my data & following the github issues #380 .. I encountered this issue -Please suggest if there is anything I should modify
-Code
tscv = TimeSeriesSplit(n_splits=5)
#Create train and test indices for each split
folds = []
splits = []
for train_index, test_index in tscv.split(x_train):
folds.append(train_index.tolist())
splits.append(test_index.tolist())
#Combine train/test indices into a list of tuples
custom_cv = {'validation_type': 'custom', 'folds': folds, 'splits': splits}
#Define AutoML with custom cross-validation
automl = AutoML(mode='Compete', total_time_limit=600, validation_strategy=custom_cv)
#Fit the model
automl.fit(x_train, y_train)
The text was updated successfully, but these errors were encountered: