Model Selection Tournament: Compare Five Algorithms
Learners will train logistic regression, random forest, XGBoost, SVM, and a neural network inside pipelines with nested CV and tabulate performance on a shared test set.
Why Compare Multiple Algorithms?
No single algorithm dominates every dataset. The No Free Lunch theorem proves that averaged over all possible problems, every algorithm performs equally well — meaning you must empirically compare algorithms on your specific data. A model selection tournament systematically trains and evaluates several diverse algorithms under identical conditions, revealing which one best fits the data's structure. The winner earns the right to hyperparameter tuning and deployment consideration.
Setting Up the Shared Pipeline Scaffold
Fair comparison requires that every algorithm starts from the same preprocessed feature matrix and is evaluated on the same held-out test set. Wrap each algorithm in a Pipeline that includes preprocessing, so preprocessing is fitted only on training folds. Use a fixed random_state everywhere for reproducibility. Keep the test set locked away — do not look at it until the final single evaluation of the tournament winner.
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split, cross_val_score
# Load preprocessed data
X, y = load_features() # returns numpy arrays after EDA cleaning
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
print(f'Training set: {X_train.shape[0]} samples')
print(f'Test set (LOCKED): {X_test.shape[0]} samples')
print(f'Positive class rate (train): {y_train.mean():.3f}')All lessons in this course
- Project Scoping: Defining the Problem and Success Criteria
- Data Wrangling and Exploratory Data Analysis
- Model Selection Tournament: Compare Five Algorithms
- Packaging, Documenting, and Presenting the Final Model