Quick Start

This guide will get you started with MLExplainer in just a few steps.

Basic Usage

Here’s a simple example using the binary classification explainer:

Import Dependencies

First, import all necessary libraries for the explainer and model training.

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from mlexplainer.explainers.shap.binary import BinaryMLExplainer

Load and Prepare Data

Load your dataset and separate features from the target variable.

# Load your data
df = pd.read_csv('your_data.csv')
X = df.drop('target', axis=1)
y = df['target']

Split Dataset

Split your data into training and testing sets for proper model validation.

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Train Model

Train your machine learning model on the training data.

# Train a Random Forest model
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

Create Explainer

Initialize the MLExplainer with your training data, features, and trained model.

# Initialize the binary classification explainer
explainer = BinaryMLExplainer(
    x_train=X_train,
    y_train=y_train,
    features=list(X_train.columns),
    model=model
)

Generate Explanations

Generate SHAP-based explanations using quantile analysis.

# Generate SHAP-based explanations with 5 quantiles
explanations = explainer.explain(q=5)

Key Concepts

Explainer Classes

Choose the appropriate explainer for your task:

  • BinaryMLExplainer for binary classification

  • MultilabelMLExplainer for multilabel classification

Feature Types

MLExplainer automatically categorizes your features into:

  • Numerical features (continuous values)

  • Categorical features (discrete categories)

  • String features (text-based, treated as categorical)

Explanation Types

  • Global: Overall feature importance across all predictions

  • Local: Individual feature contributions for specific instances

Next Steps