model_evaluation

Comprehensive guide for evaluating models, tuning hyperparameters, and selecting the best model using scikit-learn's model selection tools.

Skills
Scientific
0

What it does and how to use it

A skill that teaches Claude Code to do scientific tasks well. It is packaged knowledge the model uses whenever it is relevant.

Install the skill (command above).
In Claude Code, describe what you need - the skill turns on by itself when the context fits.
No commands to memorize: you talk in plain language and the skill does the rest.

Install (Claude Code)

mkdir -p .claude/skills/model-evaluation && curl -fsSL https://doutorai.pt/data/lib/skills__model-evaluation.md -o .claude/skills/model-evaluation/SKILL.md

Paste this command into the terminal, in your project folder, and the component installs automatically.

Content

# Model Selection and Evaluation Reference

## Overview

Comprehensive guide for evaluating models, tuning hyperparameters, and selecting the best model using scikit-learn's model selection tools.

## Train-Test Split

### Basic Splitting

```python
from sklearn.model_selection import train_test_split

# Basic split (default 75/25)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# With stratification (preserves class distribution)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, stratify=y, random_state=42
)

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