ClfbAR

Classification By Association Rules Mining (CARS) Algorithm

Algorithm adapted from Liu et al. 1998

Features of our CARS Classifier:

• High explainability in the form of Decision Rules
• Achieves comparable perfromance to more advanced classifiers on small datasets (See Benchmarks)
• Intrinsic Null handling capabilities (no need for imputations or dropping)
• Handles numeric data using binning functionalities

Usage:

Using CARS Classifier

from clfbar.clfbar import CarClassifier

### Set MinSup and MinConf or use defaults
c = CarClassifier(0.8,0.6)

### Fit the classifier
c.fit(X_train,y_train)

### Display Association rules learnt from the training data
c.rules

### Predict on test data
c.predict(X_val)

Using Binning Helpers: Transform Pandas Series from numeric dtypes into categoriecal dtype

1. Use Fisher-Jenks Binning Algorithm

from clfbar.binners import jenks_binner

### Set MAX_CLASSES & THRESHOLD or use default values
jnb = jenks_binner(5, 0.8)

### Fit and Transform numeric data into categorries
transformed_ser = jnb.fit(ser)

### Use binning from training without fitting on new data
transformed_test_ser = jnb.transform(test_ser)

2. Use Equal Frequency Binning

transformed_data = equal_freq_binner(data_frame, bins=5)

Instalation:

Using pypi(preferred)

pip install -i https://test.pypi.org/simple/ clfbar==0.0.2

Using github

git clone https://github.com/DChops/ClfbAR.git

References

• Liu, B., Hsu, W., & Ma, Y. (1998). Integrating Classification and Association Rule Mining. KDD.
• jenkspy library: https://pypi.org/project/jenkspy/