القائمة
Master machine learning foundations - algorithms, preprocessing, feature engineering, and evaluation
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-machine-learning --skill ml-fundamentalsيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
| name | ml-fundamentals |
| description | Master machine learning foundations - algorithms, preprocessing, feature engineering, and evaluation |
| version | 1.4.0 |
| sasmp_version | 1.4.0 |
| bonded_agent | 01-ml-fundamentals |
| bond_type | PRIMARY_BOND |
| parameters | {"required":[{"name":"dataset","type":"dataframe","validation":"non-empty, numeric or categorical columns"}],"optional":[{"name":"target_column","type":"string","default":null},{"name":"test_size","type":"float","default":0.2,"validation":"0.1 <= x <= 0.4"}]} |
| retry_logic | {"strategy":"exponential_backoff","max_attempts":3,"base_delay_ms":1000} |
| logging | {"level":"info","metrics":["execution_time","memory_usage","data_shape"]} |
Master the building blocks of machine learning: from raw data to trained models.
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
# 1. Load and split data
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
# 2. Create pipeline
pipeline = Pipeline([
('scaler', StandardScaler()),
('classifier', RandomForestClassifier(random_state=42))
])
# 3. Train and evaluate
pipeline.fit(X_train, y_train)
score = pipeline.score(X_test, y_test)
print(f"Accuracy: {score:.4f}")
| Step | Purpose | Implementation |
|---|---|---|
| Missing Values | Handle NaN/None | SimpleImputer(strategy='median') |
| Scaling | Normalize ranges | StandardScaler() or MinMaxScaler() |
| Encoding | Convert categories | OneHotEncoder() or LabelEncoder() |
| Outliers | Remove extremes | IQR method or Z-score |
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
sklearn.impute SimpleImputer
numeric_features = [, , ]
categorical_features = [, , ]
preprocessor = ColumnTransformer([
(, Pipeline([
(, SimpleImputer(strategy=)),
(, StandardScaler())
]), numeric_features),
(, Pipeline([
(, SimpleImputer(strategy=, fill_value=)),
(, OneHotEncoder(handle_unknown=))
]), categorical_features)
])
| Technique | Use Case | Example |
|---|---|---|
| Polynomial | Non-linear relationships | PolynomialFeatures(degree=2) |
| Binning | Discretize continuous | KBinsDiscretizer(n_bins=5) |
| Log Transform | Right-skewed data | np.log1p(x) |
| Interaction | Feature combinations | x1 * x2 |
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report
# Cross-validation
cv_scores = cross_val_score(model, X, y, cv=5, scoring='f1_weighted')
print(f"CV F1: {cv_scores.mean():.4f} (+/- {cv_scores.std()*2:.4f})")
# Detailed report
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
| Strategy | When to Use |
|---|---|
KFold | Standard, balanced data |
StratifiedKFold | Imbalanced classification |
TimeSeriesSplit | Temporal data |
GroupKFold | Grouped samples |
# TODO: Create a pipeline that:
# 1. Imputes missing values
# 2. Scales features
# 3. Trains a logistic regression
# TODO: Implement 5-fold stratified CV
# and report mean and std of F1 score
import pytest
import numpy as np
from sklearn.datasets import make_classification
def test_preprocessing_pipeline():
"""Test preprocessing handles missing values."""
X, y = make_classification(n_samples=100, n_features=10)
X[0, 0] = np.nan # Introduce missing value
pipeline = create_preprocessing_pipeline()
X_transformed = pipeline.fit_transform(X)
assert not np.isnan(X_transformed).any()
assert X_transformed.shape[0] == X.shape[0]
def test_no_data_leakage():
"""Verify preprocessing doesn't leak test data."""
X_train, X_test = X[:80], X[80:]
pipeline.fit(X_train)
X_test_transformed = pipeline.transform(X_test)
# Check that test transform uses train statistics
assert pipeline.named_steps['scaler'].mean_ is not None
| Problem | Cause | Solution |
|---|---|---|
NaN in prediction | Missing imputer | Add SimpleImputer to pipeline |
Shape mismatch | Inconsistent features | Use ColumnTransformer |
Memory error | Too many one-hot features | Use max_categories or hashing |
Poor CV variance | Data leakage | Check preprocessing order |
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