Support Vector Machine - Scikit-learn Typing CST Test
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Support Vector Machine — Scikit-learn Code
Simple SVM classifier example.
from sklearn.svm import SVC
import numpy as np
x_train = np.array([[0,0],[1,1],[0,1],[1,0]])
y_train = np.array([0,1,1,0])
model = SVC()
model.fit(x_train,y_train)
y_pred = model.predict([[1,0]])
print('Predicted class:', y_pred[0])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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