Intro to Scikit-learn

What is Scikit-learn?

Scikit-learn is a free, open-source machine learning library for Python. It provides simple and efficient tools for data analysis and modeling.

When to Use Scikit-learn

When you need standard machine learning algorithms
For data preprocessing and feature engineering
When working with small to medium datasets
For quick prototyping and model comparison

Why Use Scikit-learn

Easy to use: Simple, consistent API
Well-documented: Extensive examples and tutorials
Production-ready: Stable and reliable
Integrates well: Works with NumPy, Pandas, and other Python libraries

How Scikit-learn Compares

Library	Best For	Learning Curve	Dataset Size
Scikit-learn	Classical ML, preprocessing	Low	Small to medium
TensorFlow	Deep learning, production	High	Large
PyTorch	Research, custom neural networks	Medium	Large

Getting Started

# Install scikit-learn
# pip install scikit-learn

# Import common modules
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, metrics, model_selection
from sklearn.ensemble import RandomForestClassifier

Basic Workflow

Simple Example

# Load a dataset
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target

# Split data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train a model
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

# Evaluate the model
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, predictions)
print(f"Model accuracy: {accuracy:.2f}")

Key Components

Estimators: Models like LinearRegression, RandomForest
Transformers: Feature processing tools
Pipelines: Chain operations together
Model Selection: Tools for validation and hyperparameter tuning

Intro to Scikit-learn

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