Here, let me Google that for you.<p>[0] &lt; <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Overfitting" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Overfitting</a> &gt;<p>&gt; Overfitting generally occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfit will generally have poor predictive performance, as it can exaggerate minor fluctuations in the data.<p>[1] &lt; <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Generalization_error#Relation_to_overfitting" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Generalization_error#Relation_...</a> &gt;<p>&gt; The concepts of generalization error and overfitting are closely related. Overfitting occurs when the learned function f_S becomes sensitive to the noise in the sample. As a result, the function will perform well on the training set but not perform well on other data from the joint probability distribution of x and y. Thus, the more overfitting occurs, the larger the generalization error.<p>Shall I fetch a ruler?