Machine Learning

 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 and

 Naïve Bayes Classifier Steps: Understand the business problem Import the library files Load the dataset Data preprocessing Split the data into train and test Build the model (Naïve Bayes classifier) Test the model Performance Measures Predict the class label for new data. 1. Understand the business problem Let’s build a classifier that predicts whether I should play tennis given the forecast. It takes four attributes to describe the forecast; namely, the outlook, the temperature, the humidity, and the presence or absence of wind. Furthermore, the values of the four attributes are qualitative (also known as categorical). p(C_k |x_1,x_2,…,x_n )= p(C_k ) ∏_(i=1)^n p(x_i |C_k ) 2. Import the library files 3. Load the dataset 4. Data preprocessing 5.Split the data into train and test 6. Build the model (Navie Bayes classifier) 7. Test the model 8 .Performance Measures 9. Predict the class label for new data.

                               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