Multilayer Perceptron

Compare a complex model with a simple baseline classifier on the same data to examine whether complexity is always beneficial.

Starting conditions

Split Iris using the same 70/30 split as above, and fit StandardScaler only on Train.

Procedure

1. Compare LogisticRegression(max_iter=500) and MLPClassifier(hidden_layer_sizes=(16,),max_iter=300,random_state=7).
2. Record Train Accuracy, Test Accuracy, and whether convergence warnings occurred.

Results to retain

• A table of both models and Test Confusion Matrices.

Starting conditions

Change only MLP's hidden_layer_sizes to (4,),(16,),(32,), and compare using the same metrics.

Procedure

1. Reuse the preceding Train/Test split and Scaler fitted on Train. Train MLPs by changing only hidden_layer_sizes to (4,), (16,), and (32,).
2. Keep max_iter=300, random_state=7, and the other settings unchanged. Record hidden size, train accuracy, test accuracy, and whether convergence warnings occur in a table.
3. Explain how Train Accuracy and Test Accuracy each change as Hidden size increases, distinguishing cases with and without warnings.

Results to retain

• A table comparing Accuracy and convergence warnings by Hidden size.

Starting conditions

Compare the same two models with split seed=11 and examine variability in a small dataset.

Procedure

1. Change only the split Seed to 11, refit the Scaler on the new Train set, and transform Train and Test.
2. Retrain the Logistic Regression and Hidden (16,) MLP from Activity 1 on the new Train set with the same settings.
3. Tabulate split seed, model name, Train Accuracy, and Test Accuracy, and compare with the two-model comparison at seed 7. Explain how changing the Test samples affected the comparison.

Results to retain

• A table of both models' Accuracy for each shared split and changes in the comparison results.