Bias Variance Tradeoff - Learning Module

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Tradeoff Visualization

Seeing the Tradeoff

Mathematics provides rigor, but visualization provides intuition. The bias-variance decomposition, expressed as equations in previous pages, becomes truly clear only when you see it in action—watching how different models behave, how errors compound, and how the sweet spot emerges.

This page is dedicated to visual understanding. We'll examine multiple graphical representations of the bias-variance tradeoff, each illuminating a different aspect of this fundamental tension. By the end, you'll be able to look at any collection of model fits and immediately diagnose whether bias or variance dominates—without computing anything.

Visualization is not just pedagogical—it's a practical skill. In real model development, plots of learning curves, validation curves, and residuals often reveal problems that summary statistics hide. The visual vocabulary you develop here will serve you throughout your machine learning career.

What You Will Learn

By the end of this page, you will master the U-shaped test error curve, understand learning curve diagnostics, interpret validation curves for hyperparameter tuning, and develop visual intuition for identifying bias and variance in model behavior. These visual skills complement the mathematical understanding from earlier pages.

The Classic U-Shaped Curve

The most iconic visualization of the bias-variance tradeoff is the U-shaped test error curve. This plot shows how training error, test error, bias, and variance evolve as model complexity increases.

The Plot Structure:

The x-axis represents model complexity (e.g., polynomial degree, number of parameters, tree depth). The y-axis represents error. We plot four curves:

Training Error: Decreases monotonically with complexity. More complex models always fit training data better.
Test Error: U-shaped. Decreases initially (reducing bias), then increases (increasing variance).
Bias²: Decreases with complexity. More expressive models can represent the truth better.
Variance: Increases with complexity. More parameters mean more sensitivity to training data.

Anatomy of the U-Curve:

Error
  ^
  |     *                                    * Test Error
  |    * *                                  *
  |   *   *                               *
  |  *     **         Optimal           **
  | *        ***       Point      *****
  |*            ******* | *******       
  |                     |
  |.......................V...................  Irreducible Error
  |* * * * * * * * * * * * * * * * * *  Training Error
  +--------------------------------------------> Complexity
      Underfitting   |    Overfitting
                     |

Key Observations:

The gap: The vertical distance between training and test error represents generalization gap—a proxy for variance.
The floor: Test error cannot go below irreducible error σ², no matter how well we tune complexity.
The minimum: The optimal complexity occurs where test error is minimized, not where training error is minimized.
Asymmetry: The curve is often asymmetric—overfitting (right side) can be worse than underfitting (left side) because variance can grow without bound.

u_shaped_curve.py
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score
 
def plot_u_shaped_curve():
    """
    Generate the classic U-shaped bias-variance tradeoff curve.
    """
    np.random.seed(42)
    
    # True function
    def f_true(x):
        return np.sin(2 * np.pi * x) + 0.5 * np.cos(4 * np.pi * x)
    
    # Generate data
    n = 50
    sigma = 0.3
    X_train = np.random.uniform(0, 1, n).reshape(-1, 1)
    y_train = f_true(X_train.ravel()) + np.random.normal(0, sigma, n)
    
    # Test set (for true test error)
    X_test = np.linspace(0, 1, 200).reshape(-1, 1)
    y_test_true = f_true(X_test.ravel())
    
    # Complexity range
    degrees = range(1, 20)
    
    train_errors = []
    test_errors = []
    bias_squared = []
    variance = []
    
    n_simulations = 100  # For bias/variance estimation
    
    for degree in degrees:
        # Training error (single dataset)
        poly = PolynomialFeatures(degree)
        X_poly = poly.fit_transform(X_train)
        X_test_poly = poly.transform(X_test)
        
        model = Ridge(alpha=0.001)
        model.fit(X_poly, y_train)
        
        train_pred = model.predict(X_poly)
        train_mse = np.mean((y_train - train_pred)**2)
        train_errors.append(train_mse)
        
        # Simulate bias and variance
        predictions = []
        for _ in range(n_simulations):
            X_sim = np.random.uniform(0, 1, n).reshape(-1, 1)
            y_sim = f_true(X_sim.ravel()) + np.random.normal(0, sigma, n)
            
            poly_sim = PolynomialFeatures(degree)
            X_sim_poly = poly_sim.fit_transform(X_sim)
            X_test_sim = poly_sim.transform(X_test)
            
            model_sim = Ridge(alpha=0.001)
            model_sim.fit(X_sim_poly, y_sim)
            predictions.append(model_sim.predict(X_test_sim))
        
        predictions = np.array(predictions)
        f_bar = predictions.mean(axis=0)
        
        # Bias² and variance
        b2 = np.mean((f_bar - y_test_true)**2)
        var = np.mean(predictions.var(axis=0))
        
        bias_squared.append(b2)
        variance.append(var)
        test_errors.append(b2 + var + sigma**2)
    
    # Plot
    fig, ax = plt.subplots(figsize=(12, 7))
    
    ax.plot(list(degrees), train_errors, 'g-', linewidth=2, label='Training Error')
    ax.plot(list(degrees), test_errors, 'r-', linewidth=2, label='Test Error (Total)')
    ax.plot(list(degrees), bias_squared, 'b--', linewidth=2, label='Bias²')
    ax.plot(list(degrees), variance, 'm--', linewidth=2, label='Variance')
    ax.axhline(y=sigma**2, color='gray', linestyle=':', linewidth=1.5, 
               label=f'Irreducible Error (σ²={sigma**2:.2f})')
    
    # Mark optimal
    opt_idx = np.argmin(test_errors)
    opt_degree = list(degrees)[opt_idx]
    ax.axvline(x=opt_degree, color='orange', linestyle='--', alpha=0.7)
    ax.scatter([opt_degree], [test_errors[opt_idx]], s=100, c='red', zorder=5,
               label=f'Optimal (degree={opt_degree})')
    
    # Annotations
    ax.annotate('Underfitting\n(High Bias)', xy=(2, 0.4), fontsize=11, 
                ha='center', color='blue')
    ax.annotate('Overfitting\n(High Variance)', xy=(17, 0.5), fontsize=11,
                ha='center', color='purple')
    
    ax.set_xlabel('Model Complexity (Polynomial Degree)', fontsize=12)
    ax.set_ylabel('Mean Squared Error', fontsize=12)
    ax.set_title('The Bias-Variance Tradeoff: U-Shaped Test Error Curve', fontsize=14)
    ax.legend(loc='upper right')
    ax.set_xlim(0, 20)
    ax.set_ylim(0, 0.8)
    ax.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.show()
    
    return degrees, train_errors, test_errors, bias_squared, variance
 
plot_u_shaped_curve()

Visualizing Multiple Training Sets

One of the most illuminating visualizations shows the same model trained on multiple different training sets. This directly illustrates variance—how much the learned function changes when we change the training data.

The Visualization:

For each complexity level, we:

Generate many training datasets from the same distribution
Fit a model to each
Plot all resulting models on the same axes
Compare to the true function

The spread of the model curves shows variance. The gap between their average and the truth shows bias.

multiple_fits.py
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
 
def visualize_multiple_fits():
    """
    Show how different training sets lead to different models,
    demonstrating bias and variance visually.
    """
    np.random.seed(42)
    
    def f_true(x):
        return np.sin(2 * np.pi * x)
    
    n = 20  # Small sample to emphasize variance
    sigma = 0.3
    x_plot = np.linspace(0, 1, 200)
    
    fig, axes = plt.subplots(1, 3, figsize=(15, 5))
    degrees = [1, 4, 15]
    titles = ['Underfitting (d=1)', 'Good Fit (d=4)', 'Overfitting (d=15)']
    colors = ['blue', 'green', 'red']
    
    for ax, degree, title, color in zip(axes, degrees, titles, colors):
        predictions = []
        
        # Plot multiple model fits
        for i in range(30):
            # Generate training data
            x_train = np.random.uniform(0, 1, n)
            y_train = f_true(x_train) + np.random.normal(0, sigma, n)
            
            # Fit polynomial
            poly = PolynomialFeatures(degree)
            X_train = poly.fit_transform(x_train.reshape(-1, 1))
            X_plot = poly.transform(x_plot.reshape(-1, 1))
            
            model = LinearRegression()
            model.fit(X_train, y_train)
            y_pred = model.predict(X_plot)
            predictions.append(y_pred)
            
            ax.plot(x_plot, y_pred, color=color, alpha=0.15, linewidth=1)
        
        predictions = np.array(predictions)
        f_bar = predictions.mean(axis=0)
        
        # Plot average prediction
        ax.plot(x_plot, f_bar, color=color, linewidth=3, 
                label='Average Prediction', linestyle='--')
        
        # Plot true function
        ax.plot(x_plot, f_true(x_plot), 'k-', linewidth=2, label='True Function')
        
        # Calculate metrics
        bias_sq = np.mean((f_bar - f_true(x_plot))**2)
        var = np.mean(predictions.var(axis=0))
        
        ax.set_title(f'{title}\nBias²={bias_sq:.3f}, Var={var:.3f}', fontsize=12)
        ax.set_xlabel('x')
        ax.set_ylabel('y')
        ax.legend(loc='upper right', fontsize=9)
        ax.set_ylim(-2, 2)
        ax.grid(True, alpha=0.3)
    
    plt.suptitle('Bias vs. Variance: Each Faint Line = One Training Set', 
                 fontsize=14, y=1.02)
    plt.tight_layout()
    plt.show()
 
visualize_multiple_fits()

What to Look For:

Underfitting (low complexity):

All model fits are similar (low variance)
The average prediction (dashed line) is far from the true function (high bias)
Models consistently miss the curve's shape in the same way

Good fit (optimal complexity):

Moderate spread of model fits
Average prediction close to truth
Individual models may be slightly off, but average is accurate

Overfitting (high complexity):

Wild variation between model fits (high variance)
Average prediction may be close to truth, but individual fits are unreliable
Models wiggle dramatically through training points

The Key Insight

In practice, you only train on one dataset and get one fit—one of those faint lines. With high variance, your single fit could be anywhere in that spread. Being 'right on average' doesn't help if your particular fit is way off. This is why variance control matters so much.

Learning Curves: Error vs. Sample Size

Learning curves plot training and test error as a function of training set size. They reveal how models behave as more data becomes available and are invaluable diagnostic tools.

Anatomy of a Learning Curve:

X-axis: Number of training examples
Y-axis: Error (training or test)
Training error curve: Typically increases with more data (harder to fit more points perfectly)
Test error curve: Typically decreases with more data (better generalization)

The Diagnostic Power:

The shape of learning curves reveals the dominant error source:

Learning Curve Patterns and Diagnoses
Pattern	Train Error	Test Error	Diagnosis	Remedy
Both high, converging	High	High (close to train)	High Bias	More complexity, better features
Large gap, closing slowly	Low	High (gap narrows with n)	High Variance	Regularize, more data
Both low, close together	Low	Low (small gap)	Good Fit	Deploy or try harder problem
Gap not closing	Low	High (persistent gap)	Severe Overfitting	Much more regularization or simpler model

learning_curves_diagnostic.py
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
 
def plot_learning_curves_comparison():
    """
    Compare learning curves for high-bias vs high-variance models.
    """
    np.random.seed(42)
    
    def f_true(x):
        return np.sin(2 * np.pi * x) + 0.5 * x**2
    
    n = 200
    sigma = 0.3
    X = np.random.uniform(0, 1, n).reshape(-1, 1)
    y = f_true(X.ravel()) + np.random.normal(0, sigma, n)
    
    models = [
        ('High Bias (Linear)', LinearRegression()),
        ('Balanced (Poly 4 + Ridge)', 
         make_pipeline(PolynomialFeatures(4), Ridge(alpha=0.1))),
        ('High Variance (Poly 15)', 
         make_pipeline(PolynomialFeatures(15), LinearRegression())),
    ]
    
    fig, axes = plt.subplots(1, 3, figsize=(15, 5))
    train_sizes_frac = np.linspace(0.1, 1.0, 10)
    
    for ax, (name, model) in zip(axes, models):
        train_sizes, train_scores, test_scores = learning_curve(
            model, X, y,
            train_sizes=train_sizes_frac,
            cv=5,
            scoring='neg_mean_squared_error',
            n_jobs=-1
        )
        
        train_mse = -train_scores.mean(axis=1)
        test_mse = -test_scores.mean(axis=1)
        train_std = train_scores.std(axis=1)
        test_std = test_scores.std(axis=1)
        
        ax.fill_between(train_sizes, train_mse - train_std, 
                        train_mse + train_std, alpha=0.2, color='blue')
        ax.fill_between(train_sizes, test_mse - test_std,
                        test_mse + test_std, alpha=0.2, color='orange')
        
        ax.plot(train_sizes, train_mse, 'b-o', label='Training Error')
        ax.plot(train_sizes, test_mse, 'r-o', label='Test Error')
        ax.axhline(y=sigma**2, color='gray', linestyle=':', 
                   label=f'Noise Floor (σ²={sigma**2:.2f})')
        
        ax.set_xlabel('Training Set Size')
        ax.set_ylabel('MSE')
        ax.set_title(name)
        ax.legend(loc='upper right')
        ax.grid(True, alpha=0.3)
        
        # Add diagnostic annotation
        final_gap = test_mse[-1] - train_mse[-1]
        final_test = test_mse[-1]
        
        if final_test > 0.2 and final_gap < 0.05:
            diagnosis = "HIGH BIAS"
        elif final_gap > 0.1:
            diagnosis = "HIGH VARIANCE"
        else:
            diagnosis = "BALANCED"
        
        ax.annotate(diagnosis, xy=(0.5, 0.95), xycoords='axes fraction',
                   ha='center', fontsize=12, fontweight='bold',
                   bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))
    
    plt.suptitle('Learning Curves: Diagnosing Bias vs. Variance', fontsize=14)
    plt.tight_layout()
    plt.show()
 
plot_learning_curves_comparison()

Interpreting Each Scenario:

High Bias (Linear model):

Training error is relatively high and doesn't decrease much with more data
Test error starts high and remains high
Gap between curves is small—both are wrong
Conclusion: More data won't help; need more complex model

Balanced (Polynomial with regularization):

Training error increases slightly with more data (natural)
Test error decreases steadily toward training error
Curves converge near the noise floor
Conclusion: Good configuration; more data would help marginally

High Variance (High-degree polynomial):

Training error is very low (near zero)
Test error is high but decreases with more data
Large gap between curves
Conclusion: Need more data or regularization; the model can fit but needs more examples to stabilize

Validation Curves: Error vs. Hyperparameters

While learning curves show error vs. sample size, validation curves show error vs. a hyperparameter that controls complexity. This is directly useful for hyperparameter tuning.

Common Hyperparameters to Vary:

Polynomial degree (higher = more complex)
Regularization strength λ (higher = less complex)
Tree depth (higher = more complex)
k in k-NN (higher = less complex)
Number of hidden units (higher = more complex)

validation_curves.py
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import validation_curve
from sklearn.linear_model import Ridge
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
 
def plot_validation_curve():
    """
    Plot validation curve showing error vs. regularization strength.
    """
    np.random.seed(42)
    
    def f_true(x):
        return np.sin(2 * np.pi * x) + 0.3 * x
    
    n = 100
    sigma = 0.3
    X = np.random.uniform(0, 1, n).reshape(-1, 1)
    y = f_true(X.ravel()) + np.random.normal(0, sigma, n)
    
    # Create polynomial pipeline
    degree = 10  # High enough to potentially overfit
    model = make_pipeline(PolynomialFeatures(degree), Ridge())
    
    # Range of regularization strengths (note: reversed so left = simple)
    alpha_range = np.logspace(-4, 3, 50)
    
    train_scores, test_scores = validation_curve(
        model, X, y,
        param_name='ridge__alpha',
        param_range=alpha_range,
        cv=5,
        scoring='neg_mean_squared_error',
        n_jobs=-1
    )
    
    train_mse = -train_scores.mean(axis=1)
    test_mse = -test_scores.mean(axis=1)
    train_std = train_scores.std(axis=1)
    test_std = test_scores.std(axis=1)
    
    fig, ax = plt.subplots(figsize=(10, 6))
    
    ax.fill_between(alpha_range, train_mse - train_std,
                    train_mse + train_std, alpha=0.2, color='blue')
    ax.fill_between(alpha_range, test_mse - test_std,
                    test_mse + test_std, alpha=0.2, color='orange')
    
    ax.semilogx(alpha_range, train_mse, 'b-', linewidth=2, label='Training Error')
    ax.semilogx(alpha_range, test_mse, 'r-', linewidth=2, label='Test Error')
    ax.axhline(y=sigma**2, color='gray', linestyle=':', 
               label=f'Noise Floor (σ²={sigma**2:.2f})')
    
    # Mark optimal
    opt_idx = np.argmin(test_mse)
    opt_alpha = alpha_range[opt_idx]
    ax.axvline(x=opt_alpha, color='green', linestyle='--', alpha=0.7)
    ax.scatter([opt_alpha], [test_mse[opt_idx]], s=100, c='red', zorder=5)
    
    # Annotations
    ax.annotate('Overfitting\n(Low regularization)', 
                xy=(1e-4, 0.15), fontsize=11, ha='center')
    ax.annotate('Underfitting\n(High regularization)',
                xy=(1e2, 0.25), fontsize=11, ha='center')
    ax.annotate(f'Optimal\nα={opt_alpha:.4f}',
                xy=(opt_alpha, test_mse[opt_idx]),
                xytext=(opt_alpha * 5, test_mse[opt_idx] + 0.05),
                arrowprops=dict(arrowstyle='->', color='green'),
                fontsize=11)
    
    ax.set_xlabel('Regularization Strength (α)', fontsize=12)
    ax.set_ylabel('Mean Squared Error', fontsize=12)
    ax.set_title('Validation Curve: Finding Optimal Regularization', fontsize=14)
    ax.legend(loc='upper left')
    ax.grid(True, alpha=0.3)
    ax.set_xlim(1e-4, 1e3)
    
    plt.tight_layout()
    plt.show()
    
    return opt_alpha
 
opt_alpha = plot_validation_curve()
print(f"Optimal regularization: α = {opt_alpha:.4f}")

Reading Validation Curves

The validation curve is essentially a slice through the U-shaped error curve at a fixed sample size. Low regularization (left) corresponds to high complexity (overfitting). High regularization (right) corresponds to low complexity (underfitting). The optimal hyperparameter sits at the minimum of the test error curve.

What the Curves Tell You:

Flat test curve: The hyperparameter doesn't matter much for this data/model combination.
Sharp minimum: The optimal value is well-defined; small deviations matter.
Wide minimum: You have a range of acceptable values; the model is robust to this hyperparameter.
Curves never meeting: Even at the optimum, there's high variance (gap persists) or high bias (both curves high).
Erratic curves: High variance in cross-validation estimates; may need more folds or more data.

The Dartboard Visualization

We introduced the dartboard analogy in Page 1. Now let's visualize it directly, showing how predictions scatter around the target for different bias-variance regimes.

The Setup:

Imagine predicting the same target value (the bullseye) many times, each time with a model trained on a different dataset. Each prediction is a "dart throw."

dartboard_visualization.py
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import numpy as np
import matplotlib.pyplot as plt
 
def plot_dartboard():
    """
    Visualize bias and variance using the dartboard analogy.
    """
    np.random.seed(42)
    
    fig, axes = plt.subplots(2, 2, figsize=(10, 10))
    
    scenarios = [
        ('Low Bias, Low Variance', 0, 0.1),      # (bias, std)
        ('High Bias, Low Variance', 1.5, 0.1),
        ('Low Bias, High Variance', 0, 0.8),
        ('High Bias, High Variance', 1.0, 0.6),
    ]
    
    for ax, (title, bias, variance) in zip(axes.ravel(), scenarios):
        # Draw target circles
        for r in [0.5, 1.0, 1.5, 2.0]:
            circle = plt.Circle((0, 0), r, fill=False, color='gray', alpha=0.5)
            ax.add_patch(circle)
        
        # Bullseye (true value)
        ax.scatter([0], [0], c='red', s=100, zorder=5, marker='x', linewidths=3)
        ax.annotate('True Value', xy=(0, 0), xytext=(0.3, 0.3),
                   fontsize=9, color='red')
        
        # Generate predictions
        n_darts = 50
        # Bias shifts the center; variance controls spread
        darts_x = np.random.normal(bias, variance, n_darts)
        darts_y = np.random.normal(0, variance, n_darts)
        
        ax.scatter(darts_x, darts_y, c='blue', alpha=0.6, s=30, label='Predictions')
        
        # Show mean prediction
        mean_x, mean_y = darts_x.mean(), darts_y.mean()
        ax.scatter([mean_x], [mean_y], c='green', s=150, marker='D',
                  edgecolors='black', zorder=5, label='Average')
        
        # Draw line from true to mean (bias)
        ax.plot([0, mean_x], [0, mean_y], 'g--', linewidth=2, alpha=0.7)
        
        ax.set_xlim(-2.5, 2.5)
        ax.set_ylim(-2.5, 2.5)
        ax.set_aspect('equal')
        ax.set_title(title, fontsize=12, fontweight='bold')
        ax.legend(loc='upper right', fontsize=8)
        ax.grid(True, alpha=0.3)
        ax.set_xlabel('Prediction Error (Dimension 1)')
        ax.set_ylabel('Prediction Error (Dimension 2)')
        
        # Add metrics
        actual_bias = np.sqrt(mean_x**2 + mean_y**2)
        actual_var = np.var(darts_x) + np.var(darts_y)
        ax.annotate(f'Bias={actual_bias:.2f}\nVar={actual_var:.2f}',
                   xy=(0.02, 0.02), xycoords='axes fraction',
                   fontsize=10, 
                   bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))
    
    plt.suptitle('Bias-Variance Dartboard: Each Blue Dot = One Training Set',
                fontsize=14, y=1.02)
    plt.tight_layout()
    plt.show()
 
plot_dartboard()

Interpreting the Dartboards:

Low Bias, Low Variance (Top-Left):

Darts clustered near the bullseye
Average (green diamond) on target
Individual predictions are reliable
This is the ideal scenario

High Bias, Low Variance (Top-Right):

Darts tightly clustered but off-center
Average is consistently wrong
Predictions are stable but systematically incorrect
Classic underfitting

Low Bias, High Variance (Bottom-Left):

Darts scattered widely around the bullseye
Average might be on target, but individual throws are far off
Being "right on average" doesn't help for any single prediction
Classic overfitting

High Bias, High Variance (Bottom-Right):

Darts scattered AND off-center
The worst of both worlds
Severely misconfigured model

Residual Analysis: Diagnosing from Errors

Residuals (prediction errors) contain rich diagnostic information. Plotting residuals reveals patterns that indicate bias, variance, or model misspecification.

Residual Plots:

Residuals vs. Fitted Values: Should show random scatter around zero
Residuals vs. Each Feature: Should show no systematic pattern
Residuals Histogram/Q-Q Plot: Should approximate normal distribution (for OLS assumptions)

residual_analysis.py
Python
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
 
def residual_analysis():
    """
    Show how residual patterns indicate model problems.
    """
    np.random.seed(42)
    
    # True nonlinear function
    def f_true(x):
        return np.sin(2 * np.pi * x) + 0.5 * x
    
    n = 100
    sigma = 0.2
    x = np.random.uniform(0, 1, n)
    y = f_true(x) + np.random.normal(0, sigma, n)
    
    fig, axes = plt.subplots(2, 3, figsize=(15, 10))
    
    models = [
        ('Linear (Underfit)', 1),
        ('Polynomial 4 (Good)', 4),
        ('Polynomial 15 (Overfit)', 15),
    ]
    
    for col, (name, degree) in enumerate(models):
        poly = PolynomialFeatures(degree)
        X_poly = poly.fit_transform(x.reshape(-1, 1))
        
        model = LinearRegression()
        model.fit(X_poly, y)
        y_pred = model.predict(X_poly)
        residuals = y - y_pred
        
        # Top row: Fit visualization
        ax_fit = axes[0, col]
        x_plot = np.linspace(0, 1, 200)
        X_plot_poly = poly.transform(x_plot.reshape(-1, 1))
        y_plot = model.predict(X_plot_poly)
        
        ax_fit.scatter(x, y, alpha=0.5, label='Data')
        ax_fit.plot(x_plot, y_plot, 'r-', linewidth=2, label='Fit')
        ax_fit.plot(x_plot, f_true(x_plot), 'g--', linewidth=2, label='True')
        ax_fit.set_title(name, fontsize=12, fontweight='bold')
        ax_fit.set_xlabel('x')
        ax_fit.set_ylabel('y')
        ax_fit.legend(loc='upper right', fontsize=8)
        ax_fit.grid(True, alpha=0.3)
        
        # Bottom row: Residuals
        ax_res = axes[1, col]
        ax_res.scatter(y_pred, residuals, alpha=0.6)
        ax_res.axhline(y=0, color='red', linestyle='--', linewidth=2)
        ax_res.set_xlabel('Fitted Values')
        ax_res.set_ylabel('Residuals')
        ax_res.set_title('Residuals vs Fitted')
        ax_res.grid(True, alpha=0.3)
        
        # Add diagnostic line showing pattern
        if degree == 1:
            z = np.polyfit(y_pred, residuals, 2)
            p = np.poly1d(z)
            y_pred_sorted = np.sort(y_pred)
            ax_res.plot(y_pred_sorted, p(y_pred_sorted), 'g-', 
                       linewidth=2, label='Trend (systematic!)')
            ax_res.legend()
            ax_res.annotate('Systematic\npattern!', 
                           xy=(0.8, 0.1), xycoords='axes fraction',
                           fontsize=11, color='red', fontweight='bold')
    
    plt.suptitle('Residual Analysis: Detecting Model Problems', fontsize=14)
    plt.tight_layout()
    plt.show()
 
residual_analysis()

Residual Pattern Interpretation

•Random scatter around zero — Good fit. Errors are noise, not systematic.
•Curved pattern — Missing nonlinearity. Need higher-degree terms or nonlinear model.
•Funnel shape — Heteroscedasticity. Error variance changes with predictions. May need log transform or weighted regression.
•Clusters or bands — Missing categorical variable or mixture of subpopulations.
•Outliers — Unusual points that don't fit the model. Investigate individually.

Residuals Diagnose Bias, Not Variance

Systematic patterns in residuals indicate bias (the model is wrong in a consistent way). Variance problems show up differently—as large residual magnitude or high variability across cross-validation folds. Use residual plots for bias detection; use learning curves for variance detection.

Building Interactive Intuition

The best way to internalize the bias-variance tradeoff is through hands-on experimentation. Here's a framework for building intuition:

The Experiment:

Generate synthetic data from a known function with known noise level
Vary model complexity systematically
Observe how training error, test error, and model fits change
Repeat with different data sizes and noise levels

What to Vary:

Experimental Factors for Bias-Variance Exploration
Factor	Low Value Effect	High Value Effect
Model Complexity	High bias, low variance, underfitting	Low bias, high variance, overfitting
Training Set Size	High variance (unstable estimates)	Low variance (stable estimates)
Noise Level (σ)	Easy problem, complex models work	Hard problem, need simple models
Regularization (λ)	Low bias, high variance	High bias, low variance
True Function Complexity	Simple models suffice	Complex models needed

Mental Simulations:

Before running code, try to predict:

"If I increase polynomial degree from 3 to 10 with 50 training points and σ=0.3, what happens to:"

Training error? (Decreases—more flexibility)
Test error? (Likely increases—overfitting)
Bias? (Decreases—better approximation possible)
Variance? (Increases—more parameters)

"If I double my training data from 50 to 100 points, what happens?"

Training error? (Increases slightly—harder to fit more points)
Test error? (Decreases—more stable estimates)
Optimal complexity? (Can now afford more complexity)

Making predictions before experimenting trains your intuition faster than passive observation.

The Expert's Eye

Experienced practitioners can often glance at a learning curve or validation curve and immediately diagnose the problem. This skill comes from seeing thousands of examples. Invest time in generating visualizations, making predictions, and checking your intuition. Over time, patterns become instantly recognizable.

Summary: Tradeoff Visualization

We've developed a rich visual vocabulary for understanding and diagnosing the bias-variance tradeoff.

Key Takeaways

•The U-shaped curve: Test error first decreases (reducing bias), then increases (growing variance) with complexity. The minimum is the sweet spot.
•Multiple fits visualization: Training on different datasets shows variance directly. Spread = variance; offset from truth = bias.
•Learning curves: Plot error vs. sample size. Diagnose high bias (converging high) vs. high variance (large gap).
•Validation curves: Plot error vs. hyperparameter. Find optimal complexity directly. Similar interpretation to U-curve.
•Dartboard analogy: Visualizes bias (off-center) and variance (spread) as dart throws around a bullseye.
•Residual analysis: Systematic patterns indicate bias; random scatter indicates good fit. Complements other diagnostics.

What's Next:

The final page of this module explores the practical implications for model selection—how to use bias-variance understanding to make principled choices about model architecture, regularization, and complexity. We'll synthesize everything into actionable guidelines for real-world ML practice.

Page Complete

You now have the visual intuition to complement your mathematical understanding of the bias-variance tradeoff. These visualization skills are directly applicable to practical model development—every time you train a model, you should be generating and interpreting these plots.