Machine Learning

  Q1. Binary classification In order to perform binary classification on a dataset (class  0  and  1 ) using a neural network, which of the options is  correct  regarding the outcomes of code snippets  a  and  b ? Here the labels of observation are in the form : [0, 0, 1...]. Common model: import tensorflow from keras.models import Sequential from keras.layers import Dense from tensorflow.keras.optimizers import SGD model = Sequential() model.add(Dense(50, input_dim=2, activation='relu', kernel_initializer='he_uniform')) opt = SGD(learning_rate=0.01) Code snippet a: model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy']) Code snippet b: mode.add(Dense(1, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) The term " Required results " in the options means that the accur

  Linear Regression

                               Machine Learning MCQs- 2 Performance Metrics, Linear Regression, Naïve Bayes Classifier  1.   The greater the value for ROC AUC, better the model: True False Ans: 1   2.   A set of data are all close to each other, and they are close to the actual value.  This set of data can be described as... Accurate Precise both Precise and accurate None of the above Ans: 3 3. The maximum value of the ROC AUC is: 0.8 0.9 1 0 Ans: 3 4. Recall can be increased by increasing the decision threshold. True or False? True False Ans: 2 5. Which of these is a good measure to decide which threshold to use? Confusion matrix F1 score ROC curve Precision & Recall versus Threshold Curve Ans: 4 6.  Which of these may have to be performed before analyzing and training the dataset? Shuffling Cross-Validation F1 Score None Ans: 1 7. For the below confusion matrix, what is the total number of training datasets?   Not 5 5 Not 5 53272