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Brian Wright 2025-12-01 15:53:45 +00:00
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commit 6c89915892
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24
ds1001_final/notebooks/Final_Project_Notebook.ipynb
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@ -256,7 +256,7 @@
"metadata": {},
"outputs": [],
"source": [
"# Scatterplot between two variables\n",
"# Scatterplot between two continuous variables\n",
"plt.figure(figsize=(10, 6))\n",
"sns.scatterplot(x='Variable1', y='Variable2', data=\"xx\") # Replace 'Variable1' and 'Variable2' with your column names\n",
"plt.title('Scatterplot of Variable1 vs Variable2')\n",
@ -324,7 +324,7 @@
"source": [
"# Divide the dataset into features and target\n",
"target = \"xx\"['TargetVariable'] # Replace 'TargetVariable' with your actual target column name and \"xx\" with your dataframe name\n",
"features = \"xx\".drop(columns=[target])\n"
"features = \"xx\".drop(columns=['TargetVariable']) # Drop the target column from features\n"
]
},
{
@ -454,7 +454,8 @@
"metadata": {},
"outputs": [],
"source": [
"mf_race.by_group #What do the results show? Change the mf_race with each subgroup and report the findings. "
"mf_race.by_group #What do the results show? Change the mf_race with each subgroup and report the findings. This means\n",
"# you should run this cell multiple times, once for each of the levels in the race variable."
]
},
{
@ -464,7 +465,8 @@
"metadata": {},
"outputs": [],
"source": [
"mf_gender.by_group #What do the results show?"
"mf_gender.by_group #What do the results show? There's only two groups here so we don't need to change anything. \n",
"# in the metric frame."
]
},
{
@ -475,7 +477,8 @@
"outputs": [],
"source": [
"# Derived fairness metrics. Be sure you understand the scale and meaning of these. Here we are calculating the \n",
"# two fairness ratios using the gender_m feature. What do the results show, is the model more or less fair with this grouping?\n",
"# two fairness ratios using the gender_m feature, which is bi-variate. What do the results show, is the model more or \n",
"# less fair with this grouping?\n",
"\n",
"dpr_gender = fairlearn.metrics.demographic_parity_ratio(y_test, y_pred, sensitive_features=X_test['gender_m'])\n",
"print(\"Demographic Parity ratio:\\t\", dpr_gender)\n",
@ -501,6 +504,17 @@
"eodds_race = fairlearn.metrics.equalized_odds_ratio(y_test, y_pred, sensitive_features=X_test.filter(regex=\"race.*\"))\n",
"print(\"Equalized Odds ratio:\\t\\t\", eodds_race)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "767ebbe9",
"metadata": {},
"outputs": [],
"source": [
"!git commit -m \"Insert Message Here\" # This will commit your changes to git. \n",
"!git push # This will push your changes to back to your remote repository on GitHub."
]
}
],
"metadata": {