Question

Task 5: Run the example codes of "Building Your First Model: K-Nearest Neighbors" and "Making predictions" sections on the Jupyter notebook at once as follows: from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(n_neighbors=1) knn.fit(X_train, y_train) X_new = np.array([[5, 2.9, 1, 0.2]]) print("X_new.shape: {}".format(X_new.shape)) prediction = knn.predict(X_new) print("Prediction: {}".format(prediction)) print("Predicted target name: {}".format( iris_dataset['target_names'][prediction])) a. Above example code predicts the input ($X_{new}$) as class setosa. Change the values in $X_{new}$ to observe when the model output different predictions (versicolor, or virginica). Find out which value has the highest impact on prediction. Include your code and results.

          Task 5: Run the example codes of "Building Your First Model: K-Nearest Neighbors" and "Making
predictions" sections on the Jupyter notebook at once as follows:
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)
X_new = np.array([[5, 2.9, 1, 0.2]])
print("X_new.shape: {}".format(X_new.shape))
prediction = knn.predict(X_new)
print("Prediction: {}".format(prediction))
print("Predicted target name: {}".format(
iris_dataset['target_names'][prediction]))
a. Above example code predicts the input ($X_{new}$) as class setosa. Change the values in $X_{new}$ to
observe when the model output different predictions (versicolor, or virginica). Find out which value
has the highest impact on prediction. Include your code and results.

Task 5: Run the example codes of "Building Your First Model: K-Nearest Neighbors" and "Making
predictions" sections on the Jupyter notebook at once as follows:
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(nneighbors=1)
knn.fit(Xtrain, ytrain)
Xnew = np.array([[5, 2.9, 1, 0.2]])
print("Xnew.shape: ".format(Xnew.shape))
prediction = knn.predict(Xnew)
print("Prediction: ".format(prediction))
print("Predicted target name: ".format(
irisdataset['targetnames'][prediction]))
a. Above example code predicts the input (Xnew) as class setosa. Change the values in Xnew to
observe when the model output different predictions (versicolor, or virginica). Find out which value
has the highest impact on prediction. Include your code and results.

Added by Marc B.

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