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Merged
Alek050 merged 1 commit into from
Apr 28, 2025
Merged

updated practicals 3 & 4 #10

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193 changes: 82 additions & 111 deletions practicals_jn_book/week_3/finalbook.ipynb
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11 changes: 10 additions & 1 deletion practicals_jn_book/week_4/finalbook.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -617,12 +617,20 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"::::{tip}\n",
"When functions or algorithms have a stochastic(random) component, setting a `random_state` ensures reproducibility of results by initializing the random number generator to a fixed state. This allows consistent outputs across multiple runs of the code.\n",
"::::\n",
"\n",
"Now, that works like a charm, but let's quickly brake down what we actually did based on the provided key-word arguments:\n",
"\n",
"1. We provided our features (`X`) and outcome (`y`) because this is the data that needs to be split in a training- and a test set. \n",
"\n",
"2. We specified a `test_size`. This refers to the relative size of the test-dataset. In this case 0.3 means the test-dataset will contain 30% of the dataset, and the training-dataset will automatically contain 70% of the dataset. \n",
"\n",
"::::{note}\n",
"Depending on the data variance and size of the dataset, a test set size between 0.1 and 0.3 is normally considered.\n",
"::::\n",
"\n",
"3. We specified a random_state. Under the hood the `train_test_split` function randomly draws from the dataset, however, by specificy a random_state we make sure that the function start at the same location every time, so that we have a constant output if we would run our code again. \n",
"\n",
"::::{important}\n",
Expand All @@ -636,6 +644,7 @@
"```\n",
"\n",
"or\n",
"\n",
"```python\n",
"scaler = StandardScaler()\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
Expand Down Expand Up @@ -870,7 +879,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Taking the next step (bonus)\n",
"## Assignment 8\n",
"\n",
"Now that you have trained simple and polynomial regression models it is time for the next step. The previous task was fairly straightforward, as our dataset only contained a limited number of features that were all linked to the outcome variable. \n",
"\n",
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