Classification – Multi-Class


Multi-Class is still being developed and is yet to be available in a release version of xplainable. Please check back soon for updates.

The following documentation is a preview of the functionality that will be available in an upcoming release of xplainable.

Using the GUI

Training a classification model with the embedded xplainable GUI is easy. Run the following lines of code, and you can configure and optimise your model within the GUI to minimise the amount of code you need to write.

Example – GUI

import xplainable as xp
import pandas as pd
import os

# Initialise your session

# Load your data
data = pd.read_csv('data.csv')

# Train your model (this will open an embedded gui)
model = xp.multiclass_classifier(data)

Using the Python API

You can also train a multi-class classification model programmatically. This works in a very similar way to other popular machine learning libraries.

You can import the XMultiClassifier class and train a model as follows:

Example – XMultiClassifier()

from xplainable.core.models import XMultiClassifier
from sklearn.model_selection import train_test_split
import pandas as pd

# Load your data
data = pd.read_csv('data.csv')
x, y = data.drop('target', axis=1), data['target']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)

# Train your model
model = XMultiClassifier(), y_train)

# Predict on the test set
y_pred = model.predict(x_test)

Example – PartitionedMultiClassifier()

from xplainable.core.models import PartitionedMultiClassifier
from xpainable.core.models import XMultiClassifier
import pandas as pd
from sklearn.model_selection import train_test_split

# Load your data
data = pd.read_csv('data.csv')
train, test = train_test_split(data, test_size=0.2)

# Instantiate the partitioned model
partitioned_model = PartitionedMultiClassifier(partition_on='partition_column')

# Train the base model
base_model = XMultiClassifier()
      train.drop(columns=['target', 'partition_column']),

# Add the base model to the partitioned model (call this '__dataset__')
partitioned_model.add_partition(base_model, '__dataset__')

# Iterate over the unique values in the partition column
for partition in train['partition_column'].unique():
      # Get the data for the partition
      part = train[train['partition_column'] == partition]
      x_train, y_train = part.drop('target', axis=1), part['target']

      # Fit the embedded model
      model = XMultiClassifier(), y)

      # Add the model to the partitioned model
      partitioned_model.add_partition(model, partition)

# Prepare the test data
x_test, y_test = test.drop('target', axis=1), test['target']

# Predict on the partitioned model
y_pred = partitioned_model.predict(x_test)