Consistent Syntax and Workflow
A core strength of scikit-learn is its consistent API, which makes learning and using its many models and transformers straightforward. Most objects in scikit-learn, whether they are used for preprocessing data or for building predictive models, share a set of standard methods: fit, transform, and predict. This uniformity means you can reliably expect how to interact with any estimator or transformer, no matter the algorithm or task.
The fit method is used to learn parameters from data, such as scaling factors or model coefficients. Transformers, like scalers or encoders, use fit to calculate statistics needed to modify data, and then apply those changes with transform. Estimators, such as classifiers and regressors, use fit to learn from training data and then make predictions with predict. This shared structure is what allows you to combine different components into a seamless workflow.
1234567891011121314151617181920212223import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression # Sample data: 2 features, binary target X = np.array([[1.0, 2.0], [2.0, 0.5], [0.5, 1.0], [3.0, 4.0]]) y = np.array([0, 1, 0, 1]) # Step 1: Standardize features scaler = StandardScaler() scaler.fit(X) X_scaled = scaler.transform(X) # Step 2: Fit logistic regression clf = LogisticRegression() clf.fit(X_scaled, y) # Predict on new data X_new = np.array([[1.5, 2.5]]) X_new_scaled = scaler.transform(X_new) prediction = clf.predict(X_new_scaled) print("Prediction:", prediction)
Because scikit-learn objects use the same method names and signatures, you can easily swap out one transformer or estimator for another without rewriting your workflow logic. This makes experimentation and iteration much faster. For example, if you want to try a different scaling technique, you only need to replace the relevant object; the rest of your code remains unchanged. Similarly, you can chain multiple transformers and estimators together, confident that each will respond to fit, transform, or predict in a predictable way.
1234567891011121314151617181920212223import numpy as np from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import LogisticRegression # Sample data: 2 features, binary target X = np.array([[1.0, 2.0], [2.0, 0.5], [0.5, 1.0], [3.0, 4.0]]) y = np.array([0, 1, 0, 1]) # Step 1: Use MinMaxScaler instead of StandardScaler scaler = MinMaxScaler() scaler.fit(X) X_scaled = scaler.transform(X) # Step 2: Fit logistic regression (no change needed) clf = LogisticRegression() clf.fit(X_scaled, y) # Predict on new data X_new = np.array([[1.5, 2.5]]) X_new_scaled = scaler.transform(X_new) prediction = clf.predict(X_new_scaled) print("Prediction:", prediction)
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A core strength of scikit-learn is its consistent API, which makes learning and using its many models and transformers straightforward. Most objects in scikit-learn, whether they are used for preprocessing data or for building predictive models, share a set of standard methods: fit, transform, and predict. This uniformity means you can reliably expect how to interact with any estimator or transformer, no matter the algorithm or task.
The fit method is used to learn parameters from data, such as scaling factors or model coefficients. Transformers, like scalers or encoders, use fit to calculate statistics needed to modify data, and then apply those changes with transform. Estimators, such as classifiers and regressors, use fit to learn from training data and then make predictions with predict. This shared structure is what allows you to combine different components into a seamless workflow.
1234567891011121314151617181920212223import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression # Sample data: 2 features, binary target X = np.array([[1.0, 2.0], [2.0, 0.5], [0.5, 1.0], [3.0, 4.0]]) y = np.array([0, 1, 0, 1]) # Step 1: Standardize features scaler = StandardScaler() scaler.fit(X) X_scaled = scaler.transform(X) # Step 2: Fit logistic regression clf = LogisticRegression() clf.fit(X_scaled, y) # Predict on new data X_new = np.array([[1.5, 2.5]]) X_new_scaled = scaler.transform(X_new) prediction = clf.predict(X_new_scaled) print("Prediction:", prediction)
Because scikit-learn objects use the same method names and signatures, you can easily swap out one transformer or estimator for another without rewriting your workflow logic. This makes experimentation and iteration much faster. For example, if you want to try a different scaling technique, you only need to replace the relevant object; the rest of your code remains unchanged. Similarly, you can chain multiple transformers and estimators together, confident that each will respond to fit, transform, or predict in a predictable way.
1234567891011121314151617181920212223import numpy as np from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import LogisticRegression # Sample data: 2 features, binary target X = np.array([[1.0, 2.0], [2.0, 0.5], [0.5, 1.0], [3.0, 4.0]]) y = np.array([0, 1, 0, 1]) # Step 1: Use MinMaxScaler instead of StandardScaler scaler = MinMaxScaler() scaler.fit(X) X_scaled = scaler.transform(X) # Step 2: Fit logistic regression (no change needed) clf = LogisticRegression() clf.fit(X_scaled, y) # Predict on new data X_new = np.array([[1.5, 2.5]]) X_new_scaled = scaler.transform(X_new) prediction = clf.predict(X_new_scaled) print("Prediction:", prediction)
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