Loss Function.ipynb

loss-function.ipynb

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          "text": [
            "MSE:  2.9582283945787943e-31\n",
            "MAE:  3.7007434154171886e-16\n",
            "R2 Score:  1.0\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "\n",
        "# Sample data\n",
        "X = [[0], [1], [2], [3], [4], [5]]\n",
        "y = [1, 3, 5, 7, 9, 11]\n",
        "\n",
        "# Create instance of Linear Regression model with default loss function (least squares)\n",
        "lr = LinearRegression()\n",
        "lr.fit(X, y)\n",
        "\n",
        "# Calculate predictions using the model\n",
        "y_pred = lr.predict(X)\n",
        "\n",
        "# Evaluate using different loss functions\n",
        "mse = mean_squared_error(y, y_pred)\n",
        "mae = mean_absolute_error(y, y_pred)\n",
        "r2 = r2_score(y, y_pred)\n",
        "\n",
        "print(\"MSE: \", mse)\n",
        "print(\"MAE: \", mae)\n",
        "print(\"R2 Score: \", r2)"
      ]
    },
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      "source": [
        "from sklearn.linear_model import HuberRegressor\n",
        "\n",
        "# Create instance of Huber Regressor model with Huber loss\n",
        "huber = HuberRegressor()\n",
        "huber.fit(X, y)\n",
        "\n",
        "# Calculate predictions using the model\n",
        "y_pred = huber.predict(X)\n",
        "\n",
        "# Evaluate using different loss functions\n",
        "mse = mean_squared_error(y, y_pred)\n",
        "mae = mean_absolute_error(y, y_pred)\n",
        "r2 = r2_score(y, y_pred)\n",
        "\n",
        "print(\"MSE: \", mse)\n",
        "print(\"MAE: \", mae)\n",
        "print(\"R2 Score: \", r2)"
      ],
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          "text": [
            "MSE:  1.6773621058552543e-20\n",
            "MAE:  1.0959936661928775e-10\n",
            "R2 Score:  1.0\n"
          ]
        }
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