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Classification – Multi-Class

warning

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

Using the GUI​

Train a classification model with the embedded xplainable GUI.

Example – GUI​

import xplainable as xp
import pandas as pd
import os

# Initialise your session
xp.initialise(api_key=os.environ['XP_API_KEY'])

# 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()
model.fit(x_train, 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)

# Train your model (this will open an embedded gui)
partitioned_model = PartitionedMultiClassifier(partition_on='partition_column')

# 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()
      model.fit(x, 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)