Learning curves in ML Python - ML Experiment: Train & Evaluate

Experiment - Learning curves

Problem:You want to understand how your model learns over time by plotting learning curves. This helps to see if the model is improving with more data or training.

Current Metrics:Training accuracy: 95%, Validation accuracy: 70%

Issue:The model shows signs of overfitting: training accuracy is much higher than validation accuracy, but you don't know how performance changes with more training data or epochs.

Your Task

Create learning curves that show training and validation accuracy over epochs to analyze model learning behavior.

Use the provided dataset and model architecture.

Do not change the model structure or dataset.

Plot learning curves using training and validation accuracy.

Hint 1

Hint 2

Hint 3

Solution

ML Python

import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical

# Load data
digits = load_digits()
X = digits.data
y = to_categorical(digits.target)

# Split data
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

# Scale data
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_val = scaler.transform(X_val)

# Build model
model = Sequential([
    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    Dense(64, activation='relu'),
    Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train model with validation
history = model.fit(X_train, y_train, epochs=30, batch_size=32, validation_data=(X_val, y_val), verbose=0)

# Plot learning curves
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Learning Curves')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

Added validation split to monitor validation accuracy during training.

Stored training history to access accuracy values per epoch.

Plotted training and validation accuracy over epochs to visualize learning curves.

Results Interpretation

Before: Training accuracy was 95%, validation accuracy was 70%, but no information about progress over epochs.

After: Learning curves show training accuracy steadily increasing to about 95%, validation accuracy improves but plateaus around 75%, indicating some overfitting.

Learning curves help you see how your model learns over time. If training accuracy is high but validation accuracy is low or plateaus, your model may be overfitting. This insight guides you to adjust training or model complexity.

Bonus Experiment

Try adding dropout layers to the model and plot new learning curves to see if overfitting reduces.

💡 Hint

Dropout randomly turns off some neurons during training, which helps the model generalize better and can improve validation accuracy.

Practice

1. What does a learning curve primarily show in machine learning?

easy

A. The hardware used for training the model

B. How model performance changes with more training data

C. The number of features in the dataset

D. The exact time a model takes to train

Solution

1. Step 1: Understand the purpose of learning curves

   Learning curves plot model performance (like accuracy or error) against the amount of training data.

2. Step 2: Identify what is shown on the curve

   The curve shows how well the model learns as it sees more data, helping to spot underfitting or overfitting.

3. Final Answer:

   How model performance changes with more training data -> Option B

4. Quick Check:

   Learning curve = model performance vs data size [OK]

Hint: Learning curves show performance vs data size [OK]

Common Mistakes:

• Confusing learning curves with training time plots
• Thinking learning curves show feature counts
• Assuming hardware affects learning curves directly

2. Which of the following is the correct way to plot a learning curve in Python using scikit-learn?

easy

A. import sklearn.plot as sp; sp.learning_curve(model, X, y)

B. from sklearn.learning import curve_plot; curve_plot(model, X, y)

C. from sklearn.model_selection import learning_curve; train_sizes, train_scores, test_scores = learning_curve(model, X, y)

D. from sklearn.model_selection import train_curve; train_curve(model, X, y)

Solution

1. Step 1: Recall the correct scikit-learn function for learning curves

   The function is learning_curve from sklearn.model_selection.

2. Step 2: Check the import and usage syntax

   from sklearn.model_selection import learning_curve; train_sizes, train_scores, test_scores = learning_curve(model, X, y) correctly imports learning_curve and calls it with model, features X, and labels y.

3. Final Answer:

   from sklearn.model_selection import learning_curve; train_sizes, train_scores, test_scores = learning_curve(model, X, y) -> Option C

4. Quick Check:

   Correct import and call = from sklearn.model_selection import learning_curve; train_sizes, train_scores, test_scores = learning_curve(model, X, y) [OK]

Hint: Use sklearn.model_selection.learning_curve to plot learning curves [OK]

Common Mistakes:

• Using wrong module or function names
• Trying to import non-existent functions
• Confusing learning_curve with train_test_split

3. Given this code snippet, what will be the shape of train_sizes if X has 100 samples?

from sklearn.model_selection import learning_curve
train_sizes, train_scores, test_scores = learning_curve(model, X, y, train_sizes=[0.1, 0.5, 1.0])

medium

A. (100,)

B. (1,)

C. (3, 100)

D. (3,)

Solution

1. Step 1: Understand train_sizes parameter

   train_sizes is given as a list with 3 values: 0.1, 0.5, and 1.0, representing fractions of the training data.

2. Step 2: Determine output shape of train_sizes

   The learning_curve function returns train_sizes as an array with length equal to the number of training sizes specified, so shape is (3,).

3. Final Answer:

   (3,) -> Option D

4. Quick Check:

   train_sizes length = number of fractions given [OK]

Hint: train_sizes shape matches number of training fractions [OK]

Common Mistakes:

• Assuming train_sizes matches number of samples
• Confusing train_sizes with train_scores shape
• Thinking train_sizes is always (100,)

4. You plotted a learning curve but see both training and validation errors are high and close. What is the likely issue?

medium

A. The model is underfitting the data

B. The model is overfitting the data

C. There is a data leakage problem

D. The training data is too large

Solution

1. Step 1: Analyze error levels on learning curve

   High training error means the model cannot fit the training data well.

2. Step 2: Compare training and validation errors

   Both errors being high and close indicates underfitting, where the model is too simple.

3. Final Answer:

   The model is underfitting the data -> Option A

4. Quick Check:

   High train & val error = underfitting [OK]

Hint: High train and val errors mean underfitting [OK]

Common Mistakes:

• Confusing underfitting with overfitting
• Assuming data leakage causes high errors
• Thinking large data size causes this pattern

5. You have a learning curve where training error is very low but validation error is high and does not improve with more data. What is the best next step?

hard

A. Try a simpler model to reduce overfitting

B. Collect more training data to improve validation

C. Increase model complexity to fit training data better

D. Stop training because the model is perfect

Solution

1. Step 1: Interpret the learning curve pattern

   Low training error but high validation error means the model fits training data too well but fails on new data, indicating overfitting.

2. Step 2: Decide the best action to fix overfitting

   Using a simpler model reduces complexity and helps generalize better, improving validation error.

3. Final Answer:

   Try a simpler model to reduce overfitting -> Option A

4. Quick Check:

   Low train error + high val error = overfitting; simplify model [OK]

Hint: Low train error + high val error means overfitting; simplify model [OK]

Common Mistakes:

• Collecting more data won't fix overfitting alone
• Increasing complexity worsens overfitting
• Assuming model is perfect with low training error