Note
Click here to download the full example code
New Label#
import pandas as pd
from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import NewLabelTrainTest
test_data = {"col1": [0, 1, 2, 3] * 10}
val_data = {"col1": [4, 5, 6, 7, 8, 9] * 10}
test = Dataset(pd.DataFrame(data=test_data), label="col1", label_type="classification_label")
val = Dataset(pd.DataFrame(data=val_data), label="col1", label_type="classification_label")
Out:
It is recommended to initialize Dataset with categorical features by doing "Dataset(df, cat_features=categorical_list)". No categorical features were passed, therefore heuristically inferring categorical features in the data.
0 categorical features were inferred
test_data = {"col1": ["a", "b", "a", "c"] * 10, "col2": [1,2,2,3]*10}
val_data = {"col1": ["a","b","d"] * 10, "col2": [1, 4, 5]*10}
test = Dataset(pd.DataFrame(data=test_data), label="col2", label_type="classification_label")
val = Dataset(pd.DataFrame(data=val_data), label="col2", label_type="classification_label")
Out:
It is recommended to initialize Dataset with categorical features by doing "Dataset(df, cat_features=categorical_list)". No categorical features were passed, therefore heuristically inferring categorical features in the data.
0 categorical features were inferred
NewLabelTrainTest().run(test, val)
Total running time of the script: ( 0 minutes 0.054 seconds)