ml-fundamentals

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"]}

ML Fundamentals Skill

Master the building blocks of machine learning: from raw data to trained models.

Quick Start

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}")

Key Topics

1. Data Preprocessing

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)
])