Consider learning a target function of the form f: R (A, B, C) that is,...

 

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Consider learning a target function of the form f: R² → (A, B, C) that is, a function with 3 discrete values defined over the 2-dimensional plane, shown below. Consider the 1-nearest neighbor learning algorithm i. Show how this training data can be partitioned into classes (clusters), by drawing in the decision surface of the figure below. [5] Circle any examples of the data that would be misclassified in a leave-one-out evaluation of this learning algorithm. [3] ii. A A A A B B C C C Consider learning a target function of the form f: R² → (A, B, C) that is, a function with 3 discrete values defined over the 2-dimensional plane, shown below. Consider the 1-nearest neighbor learning algorithm i. Show how this training data can be partitioned into classes (clusters), by drawing in the decision surface of the figure below. [5] Circle any examples of the data that would be misclassified in a leave-one-out evaluation of this learning algorithm. [3] ii. A A A A B B C C C

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the assuptions are as follows Decision trees Handle real valued attribut... View the full answer