Skip to content

Commit 266d856

Browse files
add autogluon
1 parent 41fb788 commit 266d856

File tree

3 files changed

+19
-7
lines changed

3 files changed

+19
-7
lines changed

Chapter5/machine_learning.ipynb

Lines changed: 9 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -2494,9 +2494,7 @@
24942494
"id": "35aec697",
24952495
"metadata": {},
24962496
"source": [
2497-
"The traditional scikit-learn approach requires extensive manual work, including data preprocessing, model selection, and hyperparameter tuning.\n",
2498-
"\n",
2499-
"In contrast, AutoGluon automates these tasks, allowing you to train and deploy accurate models with minimal code."
2497+
"The traditional scikit-learn approach requires extensive manual work, including data preprocessing, model selection, and hyperparameter tuning."
25002498
]
25012499
},
25022500
{
@@ -2544,6 +2542,14 @@
25442542
"```"
25452543
]
25462544
},
2545+
{
2546+
"cell_type": "markdown",
2547+
"id": "d50ebd2f",
2548+
"metadata": {},
2549+
"source": [
2550+
"In contrast, AutoGluon automates these tasks, allowing you to train and deploy accurate models with minimal code."
2551+
]
2552+
},
25472553
{
25482554
"cell_type": "markdown",
25492555
"id": "25742686",

docs/Chapter5/machine_learning.html

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1943,7 +1943,6 @@ <h2><span class="section-number">6.5.17. </span>Rapid Prototyping and Comparison
19431943
<section id="autogluon-fast-and-accurate-ml-in-3-lines-of-code">
19441944
<h2><span class="section-number">6.5.18. </span>AutoGluon: Fast and Accurate ML in 3 Lines of Code<a class="headerlink" href="#autogluon-fast-and-accurate-ml-in-3-lines-of-code" title="Permalink to this heading">#</a></h2>
19451945
<p>The traditional scikit-learn approach requires extensive manual work, including data preprocessing, model selection, and hyperparameter tuning.</p>
1946-
<p>In contrast, AutoGluon automates these tasks, allowing you to train and deploy accurate models with minimal code.</p>
19471946
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.impute</span> <span class="kn">import</span> <span class="n">SimpleImputer</span>
19481947
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">OneHotEncoder</span><span class="p">,</span> <span class="n">StandardScaler</span>
19491948
<span class="kn">from</span> <span class="nn">sklearn.compose</span> <span class="kn">import</span> <span class="n">ColumnTransformer</span>
@@ -1982,6 +1981,7 @@ <h2><span class="section-number">6.5.18. </span>AutoGluon: Fast and Accurate ML
19821981
<span class="n">grid_search</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
19831982
</pre></div>
19841983
</div>
1984+
<p>In contrast, AutoGluon automates these tasks, allowing you to train and deploy accurate models with minimal code.</p>
19851985
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">autogluon.tabular</span> <span class="kn">import</span> <span class="n">TabularPredictor</span>
19861986

19871987
<span class="n">predictor</span> <span class="o">=</span> <span class="n">TabularPredictor</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;class&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">)</span>

docs/_sources/Chapter5/machine_learning.ipynb

Lines changed: 9 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -2494,9 +2494,7 @@
24942494
"id": "35aec697",
24952495
"metadata": {},
24962496
"source": [
2497-
"The traditional scikit-learn approach requires extensive manual work, including data preprocessing, model selection, and hyperparameter tuning.\n",
2498-
"\n",
2499-
"In contrast, AutoGluon automates these tasks, allowing you to train and deploy accurate models with minimal code."
2497+
"The traditional scikit-learn approach requires extensive manual work, including data preprocessing, model selection, and hyperparameter tuning."
25002498
]
25012499
},
25022500
{
@@ -2544,6 +2542,14 @@
25442542
"```"
25452543
]
25462544
},
2545+
{
2546+
"cell_type": "markdown",
2547+
"id": "d50ebd2f",
2548+
"metadata": {},
2549+
"source": [
2550+
"In contrast, AutoGluon automates these tasks, allowing you to train and deploy accurate models with minimal code."
2551+
]
2552+
},
25472553
{
25482554
"cell_type": "markdown",
25492555
"id": "25742686",

0 commit comments

Comments
 (0)