Question

Question 2 Consider the one-dimensional data set shown below.

x	0.5	3.0	4.5	4.6	4.9	5.2	5.3	5.5	7.0	9.5
y	-	-	+	+	+	-	-	+	-	-

Classify the data point x = 5.0 according to its 1st, 3rd, 5th, and 9th nearest neighbors using K-nearest neighbor classifier.

Question 3 Use data set mushrooms.csv available for developing supervised model. The data set contains two classes namely,edible and poisonous. Perform following analysis on the data set.

Data Set:

1. Understand distribution of classes in the data set using suitable plots.

2. Develop supervised models: Decision tree and k-nearest neighbor

3. Identify best k in Cross-validation evaluating method for supervised models in step 3.

4. Discuss results achieved by each supervised model using confusion matrix, sensitivity, specificity,accuracy, F1-score and ROC curve.

5. Provide your opinion on why there exist variation in performance by models.

Answer 1

Answer 2:

1-nearest neighbor: +

3-nearest neighbor: −

5-nearest neighbor: +

9-nearest neighbor: −

Answer 3:

(1)

Distribution of classes

In [20]:

print(classification_report(y_test, y_pred))

             precision    recall  f1-score   support

          0       0.85      0.97      0.91      1257
          1       0.97      0.82      0.89      1181

avg / total       0.91      0.90      0.90      2438

2(i) Decision Tree Model

In: from sklearn.tree import DecisionTreeClassifier as DT

classifier = DT(criterion='entropy',random_state=42)
classifier.fit(X_train,y_train)

Out: DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=42,
            splitter='best')

(ii)k-nearest neighbor model

In: from sklearn.neighbors import KNeighborsClassifier as KNN

classifier = KNN()
classifier.fit(X_train,y_train)

Out:

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=5, p=2,
           weights='uniform')

(4)

Decision Tree Results

In:

print_score(classifier,X_train,y_train,X_test,y_test,train=False)

Test results:

Accuracy Score: 0.9007

Classification Report:
             precision    recall  f1-score   support

          0       0.90      0.91      0.90      1257
          1       0.91      0.89      0.90      1181

avg / total       0.90      0.90      0.90      2438


Confusion Matrix:
[[1147  110]
 [ 132 1049]]

K-NN Test Results

In :

print_score(classifier,X_train,y_train,X_test,y_test,train=False)

Test results:

Accuracy Score: 0.9307

Classification Report:
             precision    recall  f1-score   support

          0       0.91      0.96      0.93      1257
          1       0.96      0.90      0.93      1181

avg / total       0.93      0.93      0.93      2438


Confusion Matrix:
[[1211   46]
 [ 123 1058]]

(5) Maybe the most well-known reason is that you have overfit the training data. You have hit upon a model, a lot of model hyperparameters, a perspective on the information, or a mix of these components and more that just so happens to give a decent ability gauge on the training dataset..