Model comparison strategies in ML Python - Model Pipeline Trace

Model Pipeline - Model comparison strategies

This pipeline shows how different machine learning models are compared to find the best one for a task. We prepare data, train multiple models, evaluate their performance, and pick the best based on metrics.

Data Flow - 6 Stages

1Data Collection

1000 rows x 10 columns→Gather raw data with features and labels→1000 rows x 10 columns

Rows of customer info with 9 features and 1 label column

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2Data Preprocessing

1000 rows x 10 columns→Clean data, handle missing values, normalize features→1000 rows x 10 columns

Normalized numeric features, no missing values

↓

3Train/Test Split

1000 rows x 10 columns→Split data into training (80%) and testing (20%) sets→Training: 800 rows x 10 columns, Testing: 200 rows x 10 columns

Training data for model learning, testing data for evaluation

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4Model Training

Training: 800 rows x 9 feature columns→Train multiple models (e.g., Logistic Regression, Decision Tree, SVM)→Trained models with learned parameters

Three models trained on same training data

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5Model Evaluation

Testing: 200 rows x 9 feature columns→Evaluate each model on test data, calculate accuracy and loss→Performance metrics for each model

Logistic Regression accuracy: 85%, Decision Tree accuracy: 80%, SVM accuracy: 88%

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6Model Selection

Performance metrics of all models→Compare metrics and select best performing model→Best model chosen for deployment

SVM selected as best model with highest accuracy

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |*   
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Initial training, model starts learning patterns
2	0.50	0.72	Loss decreases, accuracy improves
3	0.40	0.80	Model learning well, metrics improving
4	0.35	0.83	Training converging, smaller improvements
5	0.30	0.85	Good performance, ready for evaluation

Prediction Trace - 4 Layers

Layer 1: Input features

Layer 2: Feature scaling

Layer 3: Model prediction

Layer 4: Class selection

Model Quiz - 3 Questions

Why do we split data into training and testing sets?

A To check how well the model performs on new data

B To make the training faster

C To increase the size of the dataset

D To reduce the number of features

Key Insight

Comparing multiple models using the same data and metrics helps find the best model for the task. Tracking training progress and evaluating on unseen data ensures the chosen model generalizes well.

Practice

1. What is the main purpose of model comparison in machine learning?

easy

A. To increase the size of the training data

B. To reduce the number of features in the dataset

C. To find the best model for the task using fair evaluation

D. To speed up the training process

Solution

1. Step 1: Understand model comparison goal

   Model comparison aims to select the best model by evaluating multiple models on the same task.

2. Step 2: Identify correct purpose

   Increasing data size, reducing features, or speeding training are not the main goals of model comparison.

3. Final Answer:

   To find the best model for the task using fair evaluation -> Option C

4. Quick Check:

   Model comparison = best model selection [OK]

Hint: Model comparison picks the best model fairly [OK]

Common Mistakes:

• Confusing model comparison with data preprocessing
• Thinking it speeds up training
• Mixing it with feature selection

2. Which of the following is the correct way to compare two models using accuracy in Python?

easy

A. print(accuracy_score(y_true, model1.predict(X)) > accuracy_score(y_true, model2.predict(X)))

B. print(model1.accuracy > model2.accuracy)

C. print(model1.predict(X) > model2.predict(X))

D. print(model1.score(X_train) == model2.score(X_test))

Solution

1. Step 1: Understand accuracy comparison

   Accuracy should be computed using true labels and predictions, not model attributes directly.

2. Step 2: Check each option

   print(accuracy_score(y_true, model1.predict(X)) > accuracy_score(y_true, model2.predict(X))) correctly uses accuracy_score with true labels and predictions for both models.

3. Final Answer:

   print(accuracy_score(y_true, model1.predict(X)) > accuracy_score(y_true, model2.predict(X))) -> Option A

4. Quick Check:

   Use accuracy_score with true labels and predictions [OK]

Hint: Compare metrics computed on true labels and predictions [OK]

Common Mistakes:

• Comparing model attributes directly
• Comparing predictions without labels
• Mixing train and test data in scoring

3. Given the code below, what will be printed?

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression

model1 = LogisticRegression()
model2 = LogisticRegression(C=0.1)

scores1 = cross_val_score(model1, X, y, cv=5)
scores2 = cross_val_score(model2, X, y, cv=5)

print(scores1.mean() > scores2.mean())

medium

A. False if model1 has higher average cross-validation score

B. True if model1 has higher average cross-validation score

C. Always True

D. SyntaxError due to missing imports

Solution

1. Step 1: Understand cross_val_score output

   cross_val_score returns an array of scores for each fold; mean() gives average performance.

2. Step 2: Compare mean scores

   The print statement compares which model has higher average score and prints True or False accordingly.

3. Final Answer:

   True if model1 has higher average cross-validation score -> Option B

4. Quick Check:

   Mean cross-val scores compared, prints True if model1 better [OK]

Hint: Cross-validation mean score comparison prints True if first is better [OK]

Common Mistakes:

• Assuming print always outputs True
• Confusing cross_val_score output with single score
• Missing that mean() is needed for average

4. You run this code to compare two models but get an error:

from sklearn.metrics import accuracy_score

acc1 = accuracy_score(y_true, model1.predict(X_test))
acc2 = accuracy_score(y_true, model2.predict(X_test))

if acc1 > acc2:
    print('Model 1 is better')
else
    print('Model 2 is better')

What is the error and how to fix it?

medium

A. IndentationError due to wrong indent; fix indentation

B. NameError because y_true is undefined; define y_true

C. TypeError because predict returns probabilities; use predict_classes

D. SyntaxError due to missing colon after else; add ':' after else

Solution

1. Step 1: Identify syntax error in if-else block

   The else statement is missing a colon at the end, causing a SyntaxError.

2. Step 2: Fix syntax by adding colon

   Add ':' after else to correct the syntax and run the code successfully.

3. Final Answer:

   SyntaxError due to missing colon after else; add ':' after else -> Option D

4. Quick Check:

   Missing colon after else causes SyntaxError [OK]

Hint: Check colons after if/else statements [OK]

Common Mistakes:

• Ignoring missing colon after else
• Assuming predict returns classes always
• Confusing indentation error with syntax error

5. You want to compare three models fairly on a small dataset. Which strategy is best to get reliable results?

hard

A. Use cross-validation with the same metric for all models

B. Train models on different datasets and compare their training loss

C. Use a single train/test split and compare accuracy

D. Compare models only by their training accuracy

Solution

1. Step 1: Understand fair comparison on small data

   Single splits can be unreliable; training accuracy is biased; different datasets cause unfairness.

2. Step 2: Choose cross-validation with same metric

   Cross-validation uses multiple splits to reduce randomness and comparing with the same metric ensures fairness.

3. Final Answer:

   Use cross-validation with the same metric for all models -> Option A

4. Quick Check:

   Cross-validation + same metric = reliable fair comparison [OK]

Hint: Cross-validation with same metric ensures fair comparison [OK]

Common Mistakes:

• Using single train/test split only
• Comparing training accuracy instead of test
• Using different datasets for each model