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Chapter3-MachineLearning/3.9_ensemble_learning.html

@@ -751,7 +751,6 @@ <h1>3.9 Ensemble learning<a class="headerlink" href="#ensemble-learning" title="
 <li><p><strong>Robustness to noise and outliers:</strong> Ensemble methods, by aggregating predictions from multiple models, are less susceptible to the impact of individual noisy or outlier-laden data points. This results in more robust predictions, particularly in the presence of imperfect or uncertain data.</p></li>
 <li><p><strong>Increased model stability:</strong>  Individual models might perform well on certain subsets of the data but poorly on others. By combining diverse models, ensembles provide a more stable and reliable prediction across various scenarios and subgroups within geospatial datasets.</p></li>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

_sources/Chapter3-MachineLearning/3.9_ensemble_learning.ipynb

@@ -13,9 +13,7 @@
     "* **Improved generalization**: As a follow up from the previous point, ensemble methods may capture complex relationships more effectively and shows improved generalization on diverse data.\n",
     "* **Robustness to noise and outliers:** Ensemble methods, by aggregating predictions from multiple models, are less susceptible to the impact of individual noisy or outlier-laden data points. This results in more robust predictions, particularly in the presence of imperfect or uncertain data.\n",
     "* **Increased model stability:**  Individual models might perform well on certain subsets of the data but poorly on others. By combining diverse models, ensembles provide a more stable and reliable prediction across various scenarios and subgroups within geospatial datasets.\n",
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-    "Below we will"
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   },
   {