Train-Test Split Example - Scikit-learn Typing CST Test
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Train-Test Split Example — Scikit-learn Code
Splits dataset into training and testing sets.
from sklearn.model_selection import train_test_split
import numpy as np
x = np.arange(10).reshape((5,2))
y = np.array([0,1,0,1,0])
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.4,random_state=42)
print('X_train:', x_train)
print('X_test:', x_test)Scikit-learn Language Guide
Scikit-learn is an open-source Python library for machine learning that provides simple and efficient tools for data mining, analysis, and predictive modeling, built on top of NumPy, SciPy, and Matplotlib.
Primary Use Cases
- ▸Supervised learning: regression and classification
- ▸Unsupervised learning: clustering, dimensionality reduction
- ▸Data preprocessing and feature engineering
- ▸Model evaluation and selection
- ▸Building ML pipelines for production-ready workflows
Notable Features
- ▸Wide variety of ML algorithms
- ▸Pipeline API for chaining preprocessing and models
- ▸Cross-validation and hyperparameter tuning tools
- ▸Integration with NumPy, Pandas, and Matplotlib
- ▸Extensive documentation and examples
Origin & Creator
Scikit-learn was created by David Cournapeau in 2007 as a Google Summer of Code project, and later developed by a community of contributors to become a widely adopted ML library in Python.
Industrial Note
Scikit-learn is widely used in industry and research for predictive modeling, data analysis, prototyping machine learning workflows, and teaching ML concepts.
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