datarobot-feature-engineering

DataRobot Feature Engineering Skill

This skill provides guidance for working with features in DataRobot, including understanding automated feature engineering, analyzing feature importance, and optimizing feature sets.

Quick Start

Most common use case: Analyze feature importance for a model

Get feature importance: get_feature_importance(model_id) to get importance scores
Analyze top features: Sort by importance and identify key drivers
Export feature list: export_feature_list(project_id) to document features

Example: "Show me the top 10 most important features for model xyz123"

When to use this skill

Use this skill when you need to:

Understand what features DataRobot creates automatically
Analyze feature importance for models
Discover which features drive predictions
Optimize feature sets for better performance
Understand feature types and transformations
Export feature lists and definitions

Key capabilities

1. Feature Discovery

Understand automated feature engineering in DataRobot
Review derived features and transformations
Identify feature types (numeric, categorical, text, date)
Explore feature relationships and interactions

2. Feature Importance Analysis

Get feature importance scores for models
Understand which features drive predictions
Compare feature importance across models
Identify redundant or low-value features

3. Feature Optimization

Select important features for model performance
Remove low-importance features to reduce complexity
Understand feature impact on predictions
Optimize feature sets for deployment

4. Feature Documentation

Export feature lists and definitions
Document feature transformations
Understand feature derivation logic
Share feature information with stakeholders

Workflow examples

Example 1: Analyze feature importance

User request: "Show me the top 10 most important features for model xyz123 and explain what they mean."

Agent workflow:

Get feature importance scores for the model
Sort features by importance (descending)
Get top 10 features with their scores
Retrieve feature metadata and descriptions
Explain what each feature represents and why it's important
Provide insights on feature relationships

Example 2: Optimize feature set for deployment

User request: "Create a simplified feature set for deployment abc123, keeping only features with importance > 0.1."

Agent workflow:

Get feature importance for the deployed model
Filter features by importance threshold (> 0.1)
Verify filtered features are sufficient for predictions
Document the optimized feature set
Update deployment configuration if needed

Using DataRobot SDK

This skill guides you to use the DataRobot Python SDK directly. Install the SDK if needed:

pip install datarobot

Key SDK Operations

Use these DataRobot SDK methods for feature analysis:

Feature Information:

model.get_features() - List all features in a model
model.get_feature_impact() - Get feature importance scores
project.get_features() - List features in a project

Feature Analysis:

feature.name - Feature name
feature.feature_type - Feature type (Numeric, Categorical, etc.)
feature.importance - Feature importance score

See the Common Patterns section below for complete examples.

Best practices

Review automated features: DataRobot creates many derived features automatically - review them
Focus on important features: Pay attention to high-importance features for insights
Understand feature types: Different feature types require different handling
Feature documentation: Document important features for stakeholders
Feature selection: Consider removing very low-importance features for simplicity
Feature stability: Consider feature stability over time, not just importance

Common patterns

Pattern 1: Feature importance analysis

import datarobot as dr
import os

# Initialize client
client = dr.Client(
    token=os.getenv("DATAROBOT_API_TOKEN"),
    endpoint=os.getenv("DATAROBOT_ENDPOINT")
)

# Get model and feature importance
model = dr.Model.get("xyz123")
feature_impact = model.get_feature_impact()

# Sort by importance
sorted_features = sorted(
    feature_impact,
    key=lambda x: x.get('impactNormalized', 0),
    reverse=True
)

# Get top 10 features
top_features = sorted_features[:10]
for feature in top_features:
    print(f"{feature['featureName']}: {feature.get('impactNormalized', 0):.3f}")

Pattern 2: Feature filtering

import datarobot as dr

# Get model and feature importance
model = dr.Model.get("xyz123")
feature_impact = model.get_feature_impact()

# Filter by importance threshold (> 0.1)
important_features = [
    f for f in feature_impact
    if f.get('impactNormalized', 0) > 0.1
]

print(f"Found {len(important_features)} features with importance > 0.1")

Feature types in DataRobot

Numeric Features

Continuous numeric values
Automatically scaled and normalized
Can be used in mathematical operations

Categorical Features

Discrete categories or labels
Automatically encoded (one-hot, target encoding)
Important for many model types

Text Features

Text data (descriptions, comments)
Automatically processed with NLP techniques
Creates multiple text-derived features

Date/Time Features

Temporal data
Automatically creates time-based features
Important for time series models

Understanding feature importance

Feature importance scores indicate:

High importance (> 0.1): Feature significantly impacts predictions
Medium importance (0.05-0.1): Feature contributes to predictions
Low importance (< 0.05): Feature has minimal impact

Note: Importance thresholds vary by model type and problem domain.

Error handling

Common errors and solutions:

Feature not found: Verify feature name and model compatibility
Importance unavailable: Some model types don't provide importance scores
Feature access errors: Check project and model permissions

SDK Setup

Install DataRobot SDK

pip install datarobot

Initialize Client

import datarobot as dr
import os

client = dr.Client(
    token=os.getenv("DATAROBOT_API_TOKEN"),
    endpoint=os.getenv("DATAROBOT_ENDPOINT", "https://app.datarobot.com")
)

About this skill