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May 13, 2023 22:56
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Loss Function.ipynb
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{ | |
"nbformat": 4, | |
"nbformat_minor": 0, | |
"metadata": { | |
"colab": { | |
"provenance": [], | |
"authorship_tag": "ABX9TyMV0zgKYtmdUfj3GUPgFMFZ", | |
"include_colab_link": true | |
}, | |
"kernelspec": { | |
"name": "python3", | |
"display_name": "Python 3" | |
}, | |
"language_info": { | |
"name": "python" | |
} | |
}, | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "view-in-github", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"<a href=\"https://colab.research.google.com/gist/flyfir248/2040002c918e54b3f3afd0c000b414f7/loss-function.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "Wu0fk1jJUPlY", | |
"outputId": "9dcb22c6-33bb-4706-c4a1-0a9534c9d893" | |
}, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"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)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"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)" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "SyU1Ua6oU6QD", | |
"outputId": "d08ea6ac-da74-4d76-abc4-f75210e7066e" | |
}, | |
"execution_count": 2, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"name": "stdout", | |
"text": [ | |
"MSE: 1.6773621058552543e-20\n", | |
"MAE: 1.0959936661928775e-10\n", | |
"R2 Score: 1.0\n" | |
] | |
} | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"source": [], | |
"metadata": { | |
"id": "QgPJFSgMU8Xi" | |
}, | |
"execution_count": null, | |
"outputs": [] | |
} | |
] | |
} |
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