Bias&Variance.ipynb

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      "source": [
        "# **Bias & Variance**"
      ],
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        "The concepts of bias and variance are essential in understanding the behavior and performance of machine learning models, particularly in the context of supervised learning. Here's a brief explanation of bias and variance, and their relationship:\n",
        "\n",
        "1. **Bias**:\n",
        "   - Bias refers to the error introduced by approximating a real-world problem with a simplified model.\n",
        "   - A high bias model tends to oversimplify the underlying patterns in the data, leading to underfitting.\n",
        "   - Underfitting occurs when the model is unable to capture the complexity of the data and performs poorly on both the training and test sets.\n",
        "   - Examples of high bias models include linear models for highly non-linear data or shallow decision trees for complex decision boundaries.\n",
        "\n",
        "2. **Variance**:\n",
        "   - Variance refers to the variability of model predictions for different instances of the training data.\n",
        "   - A high variance model is overly sensitive to the noise or random fluctuations in the training data, leading to overfitting.\n",
        "   - Overfitting occurs when the model captures the noise or random fluctuations in the training data rather than the underlying patterns, resulting in poor generalization to unseen data.\n",
        "   - Examples of high variance models include deep neural networks with many parameters that can memorize the training data.\n",
        "\n",
        "3. **Bias-Variance Tradeoff**:\n",
        "   - The bias-variance tradeoff represents the balance between bias and variance in machine learning models.\n",
        "   - Increasing model complexity reduces bias but increases variance, and vice versa.\n",
        "   - The goal is to find the optimal balance between bias and variance to achieve good generalization performance on unseen data.\n",
        "   - Techniques such as regularization, cross-validation, and ensemble learning can help manage the bias-variance tradeoff by controlling model complexity and reducing overfitting.\n",
        "\n",
        "In summary, bias and variance are two sources of error in machine learning models that need to be carefully managed to achieve good generalization performance. Understanding the bias-variance tradeoff is crucial for selecting appropriate models, tuning hyperparameters, and improving model performance."
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      "source": [
        "# **Example**\n",
        "\n",
        "We generate synthetic training data with added noise using a sine function and then generate synthetic test data without noise.\n",
        "\n",
        "We fit polynomial regression models of different degrees (1, 3, and 9) to the training data.\n",
        "\n",
        "For each model, we make predictions on the test data and calculate the mean squared error (MSE) between the true function and the model's predictions.\n",
        "\n",
        "We plot the true function, the training data, and the model's predictions for each degree of the polynomial regression model.\n",
        "\n",
        "________________________________________\n",
        "\n",
        "The resulting plot illustrates the concepts of bias and variance:\n",
        "\n",
        "- The model with a low degree (degree 1) has high bias and low variance. It oversimplifies the true function and fails to capture its complexity.\n",
        "\n",
        "- The model with a moderate degree (degree 3) achieves a good balance between bias and variance. It closely approximates the true function and generalizes well to unseen data.\n",
        "\n",
        "- The model with a high degree (degree 9) has low bias and high variance. It fits the training data closely but fails to generalize to unseen data, resulting in overfitting."
      ],
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    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "QiV5lGN3eo-P"
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      "execution_count": 2,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Function to generate synthetic data with added noise\n",
        "def generate_data(n_samples, noise):\n",
        "    X = np.linspace(0, 5, n_samples)\n",
        "    y = np.sin(X) + noise * np.random.randn(n_samples)\n",
        "    return X.reshape(-1, 1), y"
      ],
      "metadata": {
        "id": "VHnwrF9jew4H"
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      "execution_count": 3,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Generate synthetic training data with noise\n",
        "np.random.seed(0)\n",
        "X_train, y_train = generate_data(n_samples=20, noise=0.1)"
      ],
      "metadata": {
        "id": "Wn_yb5tSe2L2"
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      "execution_count": 4,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Generate synthetic test data without noise\n",
        "X_test, y_test = generate_data(n_samples=100, noise=0)"
      ],
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      "execution_count": 5,
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        "# Fit polynomial regression models of different degrees\n",
        "degrees = [1, 3, 9]\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "for i, degree in enumerate(degrees):\n",
        "    # Define the polynomial regression pipeline\n",
        "    model = Pipeline([\n",
        "        ('poly', PolynomialFeatures(degree=degree)),\n",
        "        ('linear', LinearRegression())\n",
        "    ])"
      ],
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        "  # Fit the model to the training data\n",
        "    model.fit(X_train, y_train)"
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              "Pipeline(steps=[('poly', PolynomialFeatures(degree=9)),\n",
              "                ('linear', LinearRegression())])"
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      "source": [
        "# Fit polynomial regression models of different degrees\n",
        "degrees = [1, 3, 9]\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "for i, degree in enumerate(degrees):\n",
        "    # Define the polynomial regression pipeline\n",
        "    model = Pipeline([\n",
        "        ('poly', PolynomialFeatures(degree=degree)),\n",
        "        ('linear', LinearRegression())\n",
        "    ])\n",
        "\n",
        "    # Fit the model to the training data\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Make predictions on the test data\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    # Calculate mean squared error (MSE)\n",
        "    mse = mean_squared_error(y_test, y_pred)\n",
        "\n",
        "    # Plot the true function\n",
        "    plt.subplot(1, len(degrees), i + 1)\n",
        "    plt.scatter(X_train, y_train, color='blue', label='Training data')\n",
        "    plt.plot(X_test, y_test, color='green', label='True function')\n",
        "\n",
        "    # Plot the model's predictions\n",
        "    plt.plot(X_test, y_pred, color='red', label='Model (degree %d)' % degree)\n",
        "    plt.title('Degree %d\\nMSE = %.2f' % (degree, mse))\n",
        "    plt.xlabel('X')\n",
        "    plt.ylabel('y')\n",
        "    plt.legend()\n"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Observations:\n",
        "\n",
        "By examining the plots, we can observe the trade-off between bias and variance:\n",
        "The model with a low degree (degree 1) has high bias and low variance, resulting in underfitting.\n",
        "The model with a moderate degree (degree 3) achieves a good balance between bias and variance, providing a reasonable fit to the data.\n",
        "The model with a high degree (degree 9) has low bias and high variance, resulting in overfitting as it closely follows the training data but performs poorly on the test data."
      ],
      "metadata": {
        "id": "9-aoAcKleeOu"
      }
    }
  ]
}