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Better answers in Linear Regression exercise #108

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27ac63e
Part 2.1: Shorter and faster way to get most frequent payment type
jorgeorpinel Feb 16, 2026
45e66d8
Part 3.2: Improve answers with more accurate and specific info.
jorgeorpinel Feb 17, 2026
9622bf0
Part 3.3: Improve answers with better explanation (of batch size mainly)
jorgeorpinel Feb 17, 2026
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23 changes: 12 additions & 11 deletions ml/cc/exercises/linear_regression_taxi.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -264,7 +264,7 @@
"print(\"How many cab companies are in the dataset? \t\tAnswer: {number}\".format(number = num_unique_companies))\n",
"\n",
"# What is the most frequent payment type?\n",
"most_freq_payment_type = training_df['PAYMENT_TYPE'].value_counts().idxmax()\n",
"most_freq_payment_type = training_df['PAYMENT_TYPE'].mode()[0]\n",
"print(\"What is the most frequent payment type? \t\tAnswer: {type}\".format(type = most_freq_payment_type))\n",
"\n",
"# Are any features missing data?\n",
Expand Down Expand Up @@ -526,14 +526,16 @@
" epochs = 20\n",
" batch_size = 50\n",
"\n",
"it takes about 5 epochs for the training run to converge to the final model.\n",
"it takes about 6 epochs for the training run to approach convergence (RMSE\n",
"under 4).\n",
"\"\"\"\n",
"print(answer)\n",
"\n",
"# How well does the model fit the sample data?\n",
"# -----------------------------------------------------------------------------\n",
"answer = '''\n",
"It appears from the model plot that the model fits the sample data fairly well.\n",
"Specificlly, the final RMSE of $3.82 represents around 16% of the mean FARE.\n",
"'''\n",
"print(answer)"
],
Expand Down Expand Up @@ -618,21 +620,20 @@
"# How did lowering the learning rate impact your ability to train the model?\n",
"# -----------------------------------------------------------------------------\n",
"answer = '''\n",
"When the learning rate is too small, it may take longer for the loss curve to\n",
"converge. With a small learning rate the loss curve decreases slowly, but does\n",
"not show a dramatic drop or leveling off. With a small learning rate you could\n",
"increase the number of epochs so that your model will eventually converge, but\n",
"it will take longer.\n",
"When the learning rate is too small, it takes longer for the loss curve to\n",
"converge. The loss curve decreases slowly, but does not show a dramatic drop or\n",
"leveling off. You could increase the number of epochs so that your model will\n",
"eventually converge, but it will take longer.\n",
"'''\n",
"print(answer)\n",
"\n",
"# Did changing the batch size effect your training results?\n",
"# -----------------------------------------------------------------------------\n",
"answer = '''\n",
"Increasing the batch size makes each epoch run faster, but as with the smaller\n",
"learning rate, the model does not converge with just 20 epochs. If you have\n",
"time, try increasing the number of epochs and eventually you should see the\n",
"model converge.\n",
"Increasing the batch size makes each epoch run faster, but this implies less\n",
"learning iterations per epoch. As with the smaller learning rate, the model\n",
"does not converge with just 20 epochs. If you try increasing the number of\n",
"epochs, eventually you should see the model converge.\n",
"'''\n",
"print(answer)"
],
Expand Down