Machine Learning

  Machine Learning MCQs 👉1.  Machine Learning MCQs - UNIT 1 👉2.  Machine Learning MCQs-2 (Performance Metrics, Linear Regression, Naïve Bayes Classifier ) 👉3.  Machine Learning MCQs-3 (Logistic Regression, KNN, SVM, Decision Tree) 👉4.  Machine Learning MCQs - 4 (Clustering, Dimensionality Reduction) 👉5. Machine Learning MCQs - 5 (Ensemble Models)

  MACHINE LEARNING Syllabus: UNIT-1 Introduction: Brief Introduction to Machine Learning, Abstraction and Knowledge Representation, Types of Machine Learning Algorithms, Definition of learning systems, Goals and applications of machine learning, Aspects of developing a learning system, Data Types, training data, concept representation, function approximation. Data Pre-processing: Definition, Steps involved in pre-processing, Techniques UNIT-2 Performance measurement of models: Accuracy, Confusion matrix, TPR, FPR, FNR, TNR, Precision, recall, F1-score, Receiver Operating Characteristic Curve (ROC) curve and AUC. Supervised Learning1: Linear Regression, Multiple Variable Linear Regression, Naïve Bayes Classifiers, Gradient Descent, Multicollinearity, Bias-Variance trade-off. UNIT-3 Supervised Learning2 : Regularization, Logistic Regression, Squashing function, KNN, Support Vector Machine. Decision Tree Learning: Representing concepts as decision trees, Recursive induction of de...

  Ensemble Learning MCQs   1.      The model which consists of a group of predictors is called a Top of Form  Group  Entity  Ensemble  Set Ans: C 2.      A Random forest is an ensemble of Decision Trees Top of Form  True  False Ans: A 3.      The steps involved in deciding the output of a Random Forest are Obtain the predictions of all individual trees Predict the class that gets the most votes Both of the above None Ans: C 4.      A hard voting classifier takes into consideration Top of Form  The probabilities of output from each classifier  The majority votes from the classifiers  The mean of the output from each classifier  The sum of the output from each classifier Ans: B 5. If each classifier is a weak learner, the ensemble can still be a strong l...