unsupervised-learning
Unsupervised learning techniques
Datos de origen
- Repositorio
- ffsshhttiikk/opencode-agents-skills
- Última actividad en el origen
- 28 de febrero de 2026 a las 22:55
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 3
- Forks
- 2
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura- name
- unsupervised-learning
- description
- Unsupervised learning techniques
- license
- MIT
- compatibility
- opencode
- metadata
- {"audience":"machine-learning-engineers","category":"artificial-intelligence"}
## What I do
- Find patterns in unlabeled data
- Cluster similar data points
- Reduce data dimensionality
- Detect anomalies
- Learn representations
- Segment data populations
## When to use me
Use me when:
- Data has no labels
- Exploratory analysis needed
- Dimensionality reduction required
- Anomaly detection needed
- Customer segmentation
- Data compression
## Key Concepts
### Clustering Methods
```python
import numpy as np
from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
from sklearn.mixture import GaussianMixture
from sklearn.metrics import silhouette_score
# K-Means
kmeans = KMeans(n_clusters=3, random_state=42)
labels = kmeans.fit_predict(X)
centers = kmeans.cluster_centers_
# Find optimal k
silhouette_scores = []
for k in range(2, 10):
kmeans = KMeans(n_clusters=k)
score = silhouette_score(X, kmeans.fit_predict(X))
silhouette_scores.append(score)
# DBSCAN (density-based)
dbscan = DBSCAN(eps=0.5, min_samples=5)
labels = dbscan.fit_predict(X)
# Hierarchical
hierarchical = AgglomerativeClustering(n_clusters=3)
labels = hierarchical.fit_predict(X)
# Gaussian Mixture (probabilistic)
gmm = GaussianMixture(n_components=3)
gmm.fit(X)
labels = gmm.predict(X)
probs = gmm.predict_proba(X)
```
### Dimensionality Reduction
```python
from sklearn.decomposition import PCA, TruncatedSVD
from sklearn.manifold import TSNE
# PCA
pca = PCA(n_components=0.95) # 95% variance
X_pca = pca.fit_transform(X)
# t-SNE (visualization)
tsne = TSNE(n_components=2, perplexity=30)
X_tsne = tsne.fit_transform(X)
# SVD (sparse data)
svd = TruncatedSVD(n_components=100)
X_svd = svd.fit_transform(X)
```
### Anomaly Detection
```python
from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor
# Isolation Forest
iso = IsolationForest(contamination=0.1)
outliers = iso.fit_predict(X)
# LOF
lof = LocalOutlierFactor(n_neighbors=20, contamination=0.1)
outliers = lof.fit_predict(X)
```
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