Ml Advanced

ML Advanced - Ensemble & Unsupervised Learning

שיטות מתקדמות: Ensemble Learning ו-Unsupervised Learning.

Quick Start - Ensemble Classification

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from sklearn.model_selection import cross_val_score

# Compare ensemble methods
models = {
    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),
    'XGBoost': XGBClassifier(n_estimators=100, random_state=42),
    'Gradient Boosting': GradientBoostingClassifier(n_estimators=100, random_state=42)
}

for name, model in models.items():
    scores = cross_val_score(model, X, y, cv=5, scoring='f1')
    print(f"{name}: F1 = {scores.mean():.3f} (+/- {scores.std()*2:.3f})")

Quick Start - Clustering

from sklearn.cluster import KMeans, DBSCAN
from sklearn.metrics import silhouette_score

# K-Means
kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)
labels = kmeans.fit_predict(X)
print(f"Silhouette Score: {silhouette_score(X, labels):.3f}")

# DBSCAN (no need to specify k!)
dbscan = DBSCAN(eps=0.5, min_samples=5)
labels = dbscan.fit_predict(X)
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
print(f"Found {n_clusters} clusters")

When This Skill Activates

Use this skill when:

• Comparing Random Forest vs XGBoost vs CatBoost
• Building ensemble models (bagging, boosting, stacking)
• Doing customer/user segmentation
• Finding anomalies or outliers
• Reducing dimensionality for visualization
• Analyzing geospatial data with clustering

Core Patterns

Pattern 1: Bagging vs Boosting

Ensemble Methods
├── Bagging (parallel, reduce variance)
│   ├── Random Forest
│   └── Bagging Classifier
│
└── Boosting (sequential, reduce bias)
    ├── AdaBoost
    ├── Gradient Boosting
    ├── XGBoost
    └── CatBoost

| Aspect | Bagging | Boosting | |--------|---------|----------| | Training | Parallel | Sequential | | Goal | Reduce variance | Reduce bias | | Overfitting | Less prone | More prone | | Speed | Faster | Slower | | Example | Random Forest | XGBoost |

Pattern 2: Random Forest Feature Importance

from sklearn.ensemble import RandomForestClassifier
import pandas as pd

# Train model
rf = RandomForestClassifier(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

# Feature importance
importance = pd.DataFrame({
    'feature': feature_names,
    'importance': rf.feature_importances_
}).sort_values('importance', ascending=False)

print(importance.head(10))

# Plot
import matplotlib.pyplot as plt
importance.head(15).plot(kind='barh', x='feature', y='importance')
plt.title('Feature Importance')
plt.show()

Pattern 3: XGBoost with Early Stopping

from xgboost import XGBClassifier

model = XGBClassifier(
    n_estimators=1000,
    learning_rate=0.1,
    max_depth=6,
    early_stopping_rounds=50,  # Stop if no improvement
    eval_metric='logloss',
    random_state=42
)

model.fit(
    X_train, y_train,
    eval_set=[(X_val, y_val)],
    verbose=False
)

print(f"Best iteration: {model.best_iteration}")

Pattern 4: CatBoost for Categorical Features

from catboost import CatBoostClassifier

# CatBoost handles categorical features natively!
cat_features = ['color', 'brand', 'category']  # Column names or indices

model = CatBoostClassifier(
    iterations=500,
    learning_rate=0.1,
    depth=6,
    cat_features=cat_features,  # No encoding needed!
    verbose=100
)

model.fit(X_train, y_train, eval_set=(X_val, y_val))

Pattern 5: K-Means with Elbow Method

from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

# Find optimal k
inertias = []
K_range = range(2, 11)

for k in K_range:
    kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)
    kmeans.fit(X)
    inertias.append(kmeans.inertia_)

# Plot elbow
plt.figure(figsize=(8, 4))
plt.plot(K_range, inertias, 'bo-')
plt.xlabel('Number of clusters (k)')
plt.ylabel('Inertia')
plt.title('Elbow Method')
plt.show()

Pattern 6: DBSCAN for Density-Based Clustering

from sklearn.cluster import DBSCAN
import numpy as np

# DBSCAN parameters:
# eps: maximum distance between neighbors
# min_samples: minimum points to form a cluster

dbscan = DBSCAN(eps=0.5, min_samples=5, metric='euclidean')
labels = dbscan.fit_predict(X)

# Results
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
n_noise = (labels == -1).sum()

print(f"Clusters: {n_clusters}, Noise points: {n_noise}")

Pattern 7: Geospatial Clustering with Haversine

from sklearn.cluster import DBSCAN
import numpy as np

# Convert lat/lon to radians (required for Haversine)
coords_rad = np.radians(df[['latitude', 'longitude']].values)

# Earth radius in km
kms_per_radian = 6371.0088
eps_km = 0.5  # Cluster radius in km
eps = eps_km / kms_per_radian

# DBSCAN with Haversine distance
dbscan = DBSCAN(
    eps=eps,
    min_samples=10,
    metric='haversine',
    algorithm='ball_tree'
)

df['cluster'] = dbscan.fit_predict(coords_rad)

Pattern 8: PCA for Dimensionality Reduction

from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

# Always scale before PCA!
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Fit PCA
pca = PCA(n_components=0.95)  # Keep 95% variance
X_pca = pca.fit_transform(X_scaled)

print(f"Original dimensions: {X.shape[1]}")
print(f"Reduced dimensions: {X_pca.shape[1]}")
print(f"Variance explained: {pca.explained_variance_ratio_.sum():.2%}")

Pattern 9: t-SNE for Visualization

from sklearn.manifold import TSNE
import matplotlib.pyplot as plt

# t-SNE (good for visualization, NOT for preprocessing)
tsne = TSNE(
    n_components=2,
    perplexity=30,  # Try 5-50
    random_state=42
)
X_tsne = tsne.fit_transform(X)

# Plot
plt.figure(figsize=(10, 8))
scatter = plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=labels, cmap='tab10')
plt.colorbar(scatter)
plt.title('t-SNE Visualization')
plt.show()

Reference Navigation

For detailed content, see:

• Ensemble Methods: reference/ensemble_methods.md - Random Forest, XGBoost, CatBoost, Stacking, TreeInterpreter
• Clustering Algorithms: reference/clustering_algorithms.md - K-Means, DBSCAN, Hierarchical, HDBSCAN
• Dimensionality Reduction: reference/dimensionality_reduction.md - PCA, t-SNE, UMAP, MDS
• Geospatial Analysis: reference/geospatial_analysis.md - Haversine, ConvexHull, KDE density plots
• Recommender Systems: reference/recommender_systems.md - Matrix Factorization, NeuMF, MovieLens patterns

Common Mistakes to Avoid

1. Not Scaling Before PCA

# WRONG: PCA on unscaled data
pca.fit_transform(X)  # Features with larger scales dominate!

# CORRECT: Scale first
X_scaled = StandardScaler().fit_transform(X)
pca.fit_transform(X_scaled)

2. Using t-SNE for Preprocessing

# WRONG: t-SNE output as features
X_tsne = TSNE().fit_transform(X)
model.fit(X_tsne, y)  # Bad! t-SNE is for visualization only

# CORRECT: Use PCA for preprocessing
X_pca = PCA(n_components=50).fit_transform(X)
model.fit(X_pca, y)

3. Ignoring DBSCAN Noise Points

# Noise points have label -1
noise_mask = labels == -1
print(f"Noise: {noise_mask.sum()} points ({noise_mask.mean():.1%})")

# Consider: too much noise? Adjust eps or min_samples

4. Wrong n_init for K-Means

# K-Means is sensitive to initialization
# WRONG: Single initialization
kmeans = KMeans(n_clusters=3, n_init=1)  # May find local minimum

# CORRECT: Multiple initializations (default is 10)
kmeans = KMeans(n_clusters=3, n_init=10)

5. Using Euclidean Distance on Lat/Lon

# WRONG: Euclidean on geographic coordinates
DBSCAN(eps=0.01, metric='euclidean').fit(lat_lon_data)

# CORRECT: Use Haversine for geographic data
coords_rad = np.radians(lat_lon_data)
DBSCAN(eps=eps_in_radians, metric='haversine').fit(coords_rad)

Teaching Mode

When explaining ensemble/clustering:

1. Bagging intuition: "Ask many experts, vote on answer"
2. Boosting intuition: "Learn from mistakes, focus on hard cases"
3. K-Means intuition: "Find k centroids that minimize point-to-center distances"
4. DBSCAN intuition: "Find dense regions separated by sparse areas"
5. PCA intuition: "Find directions of maximum variance, project data onto them"

Clustering Decision Tree

Need to cluster data?
├── Know number of clusters (k)?
│   ├── Yes → K-Means
│   └── No → DBSCAN or Hierarchical
│
├── Data has varying densities?
│   ├── Yes → DBSCAN or HDBSCAN
│   └── No → K-Means
│
├── Need hierarchical structure?
│   └── Yes → Hierarchical Clustering
│
└── Geographic data?
    └── Yes → DBSCAN with Haversine

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: levy-n
• Source: levy-n/claude-useful-skills
• License: MIT

Install and usage instructions live in the source repository linked above.