Lära AutoML Workflow Overview | Fundamentals of AutoML

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The AutoML workflow automates several steps in the machine learning process to streamline building effective models. The main stages typically include:

Data preprocessing: clean, transform, and prepare data for modeling;
Feature engineering: create, select, or transform features to boost model performance;
Model selection: choose the best algorithm for the task;
Hyperparameter tuning: optimize algorithm settings for best results;
Evaluation: assess model performance on unseen data.

Each of these steps can be a source of complexity and potential error when done manually, which is why AutoML systems focus on automating them for you.

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.ensemble import RandomForestClassifier

# Chain preprocessing and model training steps in a pipeline
pipeline = Pipeline([
    ("imputer", SimpleImputer(strategy="mean")),
    ("scaler", StandardScaler()),
    ("classifier", RandomForestClassifier(n_estimators=100, random_state=42))
])

# Example usage:
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)

Note

Always use Pipeline objects to combine preprocessing and modeling steps. This prevents data leakage by ensuring that transformations are learned only on training data and then applied to validation or test sets.

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