Hi, I need help for the below question on the coding. Previously I received an answer for my KNN function in Chegg here. So now my issue is I unable to perform the KNN class for the confusion matrix in my next code cell. Please help me on the coding for my question in PART 3.

Let me show you the KNN class function below: THIS PART NO NEED TO PROVIDE CODING, IS JUST TO SHOW YOU ONLY.

class KNN:

"""

Class to store data for regression problems

"""

def __init__(self, x_train, y_train, K=5):

"""

Creates a kNN instance

:param x_train: numpy array with shape (n_rows,1)- e.g. [[1,2],[3,4]]

:param y_train: numpy array with shape (n_rows,)- e.g. [1,-1]

:param K: The number of nearest points to consider in classification

"""

# Import and build the BallTree on training features

from sklearn.neighbors import BallTree

self.balltree = BallTree(x_train)

# Cache training labels and parameter K

self.y_train = y_train

self.K = K

def majority(self, neighbor_indices, neighbor_distances=None):

"""

Given indices of nearest neighbors in training set, return the majority label.

Break ties by considering 1 fewer neighbor until a clear winner is found.

:param neighbor_indices: The indices of the K nearest neighbors in self.X_train

:param neighbor_distances: Corresponding distances from query point to K nearest neighbors.

"""

# your code here

K = self.K

while True:

labels = [self.y_train[i] for i in neighbor_indices[:K]]

label_counts = Counter(labels)

majority_label, count = label_counts.most_common(1)[0]

if count > K/2:

return majority_label

K -= 1

def classify(self, x):

"""

Given a query point, return the predicted label

:param x: a query point stored as an ndarray

"""

# your code here

distances, indices = self.balltree.query(x.reshape(1, -1), k=self.K)

return self.majority(indices[0])

def predict(self, X):

"""

Given an ndarray of query points, return yhat, an ndarray of predictions

:param X: an (m x p) dimension ndarray of points to predict labels for

"""

# your code here

m, p = X.shape

yhat = np.zeros(m)

for i in range(m):

yhat[i] = self.classify(X[i])

return yhat

MY QUESTION AND MY ERROR:

Part 3 : Checking how well your classifier does Use your KNN class to perform KNN on the validation data with =3 and do the following:

MY ANSWER:

from sklearn import neighbors

from sklearn.metrics import confusion_matrix

import sklearn.metrics as metrics

import seaborn as sns

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

knn = neighbors.KNeighborsClassifier(n_neighbors=3)

knn.fit(x_train,y_train)

val_yhat = knn.predict(val_x)

cm = confusion_matrix(val_y, val_yhat)

f, ax =plt.subplots(figsize = (5,5))

sns.heatmap(cm,annot = True, linewidths= 0.5, linecolor="red", fmt=".0f", ax=ax)

plt.xlabel("y_pred")

plt.ylabel("y_true")

plt.show()

print(metrics.confusion_matrix(val_y, val_yhat))

print (metrics.classification_report(val_y,val_yhat))

My first submission error was: reshape

My second submission error was:

ValueError: Found input variables with inconsistent numbers of samples: [3, 70000]

My third submission error was:

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

I don't know how to code already. Please help me on the coding for the prediction of KNN class with Confusion Matrix. Thank you so much for the help.