major upload of (python) course material & solutions

Machine Learning for Economics and Finance/02_Supervised Learning - Classification/02_Default_data.ipynb

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+    "\\vspace{-4cm}\n",
+    "\\begin{center}\n",
+    "  \\LARGE{Machine Learning for Economics and Finance}\\\\\n",
+    "  \\Large{Task 1: Logistic Regressions}\\\\[0.5cm]\n",
+    "  \\Large{\\textbf{02\\_Default\\_data}}\\\\[1.0cm]\n",
+    "  \\large{Ole Wilms}\\\\[0.5cm]\n",
+    "  \\large{July 29, 2024}\\\\\n",
+    "\\end{center}"
+   ]
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+   "source": [
+    "## Task 1: Logistic Regressions"
+   ]
+  },
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+   "cell_type": "markdown",
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+    "1.1 Randomly split the data into $7000$ observations for training and $3000$ observations for testing and set the seed to $1$ before sampling the data. Call these two datasets *train_data* and *test_data* respectively. (Hint: use the code to split the data from 01 Auto_data_2.R or Auto_data_2.Rmd)"
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+   "execution_count": null,
+   "id": "335aa198-5a94-4c5a-8ad8-67c78bcf71f5",
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+   "source": []
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+   "source": [
+    "1.2 Fit a logistic regression of default on *income* using the *train_data*. Analyze the significance of\n",
+    "the estimated coefficients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e38a201-7f2d-4999-beab-5739217a9318",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43c6dade-5a22-476a-b3bf-bfd1b880038d",
+   "metadata": {
+    "tags": [],
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+   },
+   "source": [
+    "1.3 Compute the *out-of-sample accuracy* and *error rate* and compare to the *in-sample statistics*. Do\n",
+    "you think this is a good model to predict default?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44028726-1eff-436f-bc47-04a6786ae3ad",
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+  },
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+   },
+   "source": [
+    "1.4 Add balance as a predictor and compute the *out-of-sample error rate* and *accuracy*. Do you\n",
+    "think this is a good model to predict *default*?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a7216df-adf5-4df0-9593-69c1a7649f64",
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+   "metadata": {
+    "tags": [],
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+   },
+   "source": [
+    "1.5 Compare the results for Task $1.4$ to a model with only balance as a predictor. Which model\n",
+    "would you choose?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28082bd5-8fe1-4160-aec0-1a92aebfa671",
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+   "source": [
+    "1.6 Take the model from Task $1.4$ but now re-estimate the model using different *seeds* to draw your\n",
+    "*training* and *test data*. Does your *test error rate* change with the seed? What’s going on here?"
+   ]
+  },
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