Evaluate the Responsible AI dashboard and see what it does.

Here's a structured list of Responsible AI Dashboard Deployment Steps using the corrected scripts. Each step includes:

• ✅ Step Number & Action
• 🎯 Purpose
• 🧪 Expected Result

bashCopyEditpip install --upgrade raiutils raiwidgets responsibleai ipywidgets

🎯 Purpose:
Install the Python packages required to run Responsible AI analysis and render the dashboard.

🧪 Expected Result:
Packages are installed without errors; dashboard widgets can render in the notebook (after kernel restart).

pythonCopyEditfrom sklearn.datasets import fetch_openml
import pandas as pd

data = fetch_openml(name='adult', version=2, as_frame=True)
df = data.frame.dropna()

🎯 Purpose:
Load a well-known classification dataset (Adult Census Income) and remove any missing values to avoid downstream errors.

🧪 Expected Result:
A clean DataFrame with no null values is loaded.

pythonCopyEditfrom sklearn.model_selection import train_test_split

target_column = 'class'
X = df.drop(columns=[target_column])
y = df[target_column]

X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)

🎯 Purpose:
Separate features and target, then split into training/testing sets for model training and evaluation.

🧪 Expected Result:
X_train, X_test, y_train, y_test variables created and stratified properly.

pythonCopyEditfrom sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier

categorical_cols = X_train.select_dtypes(include=['object', 'category']).columns.tolist()
numerical_cols = X_train.select_dtypes(include=['int64', 'float64']).columns.tolist()

preprocessor = ColumnTransformer([
    ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_cols),
    ('num', StandardScaler(), numerical_cols)
])

clf = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', RandomForestClassifier(n_estimators=10, random_state=42))
])

clf.fit(X_train, y_train)

🎯 Purpose:
Build a model pipeline that encodes categorical features, scales numeric ones, and trains a classifier.

🧪 Expected Result:
Pipeline is trained successfully on the training data without conversion errors.

pythonCopyEdit# Ensure target column is a supported type
y_train_clean = y_train.astype(str)
y_test_clean = y_test.astype(str)

train_data = X_train.copy()
train_data[target_column] = y_train_clean

test_data = X_test.copy()
test_data[target_column] = y_test_clean

🎯 Purpose:
Re-attach the target column (as string) to the feature DataFrames — required for RAIInsights.

🧪 Expected Result:
train_data and test_data DataFrames contain all required columns including the target.

pythonCopyEditfrom responsibleai import RAIInsights, FeatureMetadata

feature_metadata = FeatureMetadata(categorical_features=categorical_cols)

rai_insights = RAIInsights(
    model=clf,
    train=train_data,
    test=test_data,
    target_column=target_column,
    task_type="classification",
    feature_metadata=feature_metadata
)

🎯 Purpose:
Create a RAIInsights object that acts as the core engine for the Responsible AI dashboard.

🧪 Expected Result:
RAIInsights object is initialized successfully and ready for configuration.

pythonCopyEditrai_insights.explainer.add()
rai_insights.error_analysis.add()
rai_insights.counterfactual.add(total_CFs=5, desired_class='opposite')
rai_insights.causal.add(treatment_features=categorical_cols)

🎯 Purpose:
Attach various tools (explanation, error analysis, counterfactuals, causal inference) to the insights engine.

🧪 Expected Result:
No errors thrown; tools are queued for computation.

pythonCopyEditrai_insights.compute()

🎯 Purpose:
Run analysis for all selected tools. This step may take a minute or more.

🧪 Expected Result:
Tool outputs are generated for the first 5,000 rows of the test set.

pythonCopyEditfrom raiwidgets import ResponsibleAIDashboard

ResponsibleAIDashboard(rai_insights)

🎯 Purpose:
Open an interactive dashboard to explore insights such as feature importance, what-if analysis, and error breakdowns.

🧪 Expected Result:
A dashboard is displayed inside the notebook. Interactive plots and controls are available for analysis.

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