Support Vector Classifier using breast cancer data

Author: tirthajyoti

Support_Vector_Machine_Practice.ipynb

@@ -9,7 +9,7 @@
     "In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a **non-probabilistic binary linear classifier** (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). An SVM model is a representation of the examples as points in space, mapped ***so that the examples of the separate categories are divided by a clear gap that is as wide as possible***. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. This gap is also called maximum margin and the SVM classifier is called ***maximum margin clasifier***.\n",
     "\n",
     "In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.\n",
-    "![SVM-1](/Images/SVM-1.png)"
+    "![SVM-1](./Images/SVM-1.png)"
    ]
   },
   {