Dr. Rambabu Pemula

  Unit – III Supervised Learning: Classification 1.     What is Classification 2.     General Approach to Classification 3.     K-NearestNeighbour Algorithm 4.     Logistic Regression 5.     Decision Trees: a.      Construction b.     classification and regression trees c.      example 6.     Naive Bayesian Classifier 7.     Support Vector Machines: a.      Optimal Separation b.     Kernels c.      Algorithm 👉 UNIT-II (A) Supervised Learning Classification Notes 👉 UNIT-III (A) Supervised Learning Classification PPTs 👉 UNIT-III(A) Important Long Answer Questions 👉 UNIT-III (A) Supervised Learning: Classification Important Questions YouTube Link

 Machine Learning Programs 👉 Data Preprocessing in Machine Learning 👉 Data Preprocessing in Machine learning (Handling Missing values ) 👉 Linear Regression - ML Program - Weight Prediction 👉 Naïve Bayes Classifier - ML Program 👉 LOGISTIC REGRESSION - PROGRAM 👉 KNN Machine Learning Program 👉 Support Vector Machine (SVM) - ML Program 👉 Decision Tree Classifier on Iris Dataset 👉 Classification of Iris flowers using Random Forest 👉 DBSCAN 👉 Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file 👉 For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples. 👉 Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree...

  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...

 Machine Learning MCQs-3  (Logistic Regression, KNN, SVM, Decision Tree) --------------------------------------------------------------------- 1. A Support Vector Machine can be used for Performing linear or nonlinear classification Performing regression For outlier detection All of the above Ans: 4 2.   The decision boundaries in a Support Vector machine is fully determined (or “supported”) by the instances located on the edge of the street?   True False Ans: 1   3.   Support Vector Machines are not sensitive to feature scaling True False Ans: 2  4.  If we strictly impose that all instances be off the street and on the right side, this is called Soft margin classification Hard margin classification Strict margin classification Loose margin classification Ans: 2 5. The main issues with hard margin classification are It only works if the data is linearly separable It is quite sensitive to outliers It is impossible to find a margin if the ...