ML

Question 1

Key classification metrics

Answer 1

Accuracy - number of correct predicitions divided by total number of predictions (useful when target classes are well balanced)
Precision - number of true positives divided by the number of true positives plus false positives
Recall - number of true positives divided by the number of true positives plus false negatives
F1 Score - harmonic mean of precision and recall F1 = 2 * (precision*recall)/(precision + recall) *harmonic mean punishes extreme values

Question 2

What is type I error?

Answer 2

false positive

Question 3

What is type II error?

Answer 3

false negative

Question 4

Confusion matrix in scikit learn

Answer 4

from sklearn.metrics import confusion_matrix

print(confusion_matrix(y_test,predictions))

Question 5

How to use a pipeline in scikit-learn

Answer 5

from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
text_clf = Pipeline([(‘tfidf’,TfidfVectorizer()),(‘clf’,LinearSVC())])
text_clf.fit(X_train,y_train)

Question 6

Classification task using Keras

Answer 6

import numpy as np

from sklearn.datasets import load_iris

iris = load_iris()

X = iris.data

y = iris.target

from keras.utils import to_categorical

y = to_categorical(y)

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state=42)

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()

scaler.fit(X_train)

scaled_X_train = scaler.transform(X_train)

scaled_X_test = scaler.transform(X_test)

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()

model. add(Dense(8,input_dim=4,activation=’relu’))
model. add(Dense(8,input_dim=4,activation=’relu’))
model. add(Dense(3,activation=’softmax’))
model. compile(loss=’categorical_crossentropy’,optimizer=’adam’,metrics=[‘accuracy’])

model.fit(scaled_X_train,y_train,epochs=150,verbose=2)

predictions = model.predict_classes(scaled_X_test)

y_test.argmax(axis=1)

from sklearn.metrics import confusion_matrix, classification_report, accuracy_score

confusion_matrix(y_test.argmax(axis=1),predictions)

print(classification_report(y_test.argmax(axis=1),predictions))