Due to unsupervised nature, the clusters have no labels. Tips for Answering Decision Making Questions. 49) What are two techniques of Machine Learning ? For example: You have 3 variables in a data set, of which 2 are correlated. Q6. Decision trees are most suitable for tabular data. Answer: Following are the methods of variable selection you can use: Q19. Bagging is a method in ensemble for improving unstable estimation or classification schemes. It is essential to invest individuals who will positively contribute to the overall work environment and consistently do their best to be effective. The weak learners’ performance is all collected and aggregated to improve the boosted tree’s overall performance. ‘People who bought this, also bought…’ recommendations seen on amazon is a result of which algorithm? You will have to read both of them carefully and then choose one of the options from the two statements’ options. The new trees introduced into the model are just to augment the existing algorithm’s performance. To combat such situation, we calculate correlation to get a value between -1 and 1, irrespective of their respective scale. Each of the in a random forest is built on all the features. For example: a gene mutation data set might result in lower adjusted R² and still provide fairly good predictions, as compared to a stock market data where lower adjusted R² implies that model is not good. So, you are bound to lose all the interpretability after you apply the random forest algorithm. You are assigned a new project which involves helping a food delivery company save more money. We start with 1 feature only, progressively adding 1 feature at a time, i.e. Only in the algorithm of random forest, real values can be handled by making them discrete. Since you can't predict what question the hiring manager will ask you, you won't be able to write a stock answer in response. Considering the long list of machine learning algorithm, given a data set, how do you decide which one to use? A classifier in a Machine Learning is a system that inputs a vector of discrete or continuous feature values and outputs a single discrete value, the class. Q40), but it is surely useful for job interviews in startups and bigger firms. We'll use the following data: A decision tree starts with a decision to be made and the options that can be taken. What will happen if you don’t rotate the components? If the trees are connected in such fashion, all the trees cannot be independent of each other, thus rendering the first statement false. Therefore, we always prefer model with minimum AIC value. In machine learning, thinking of building your expertise in supervised learning would be good, but companies want more than that. Explain feature selection using information gain/entropy technique? Since logistic regression is used to predict probabilities, we can use AUC-ROC curve along with confusion matrix to determine its performance. What lead you to choose this career path? The generation of random forests is based on the concept of bagging. They both can easily handle the features which have real values in them. We can assign weight to classes such that the minority classes gets larger weight. Q23. The values which are obtained after taking out the subsets are then fed into singular decision trees. Maximum Likelihood helps in choosing the the values of parameters which maximizes the likelihood that the parameters are most likely to produce observed data. Hi Sampath, Before the interview, think about moments where you really excelled professionally. Your model R² isn’t as good as you wanted. The possibility of overfitting exists as the criteria used for training the model is not the same as the criteria used to judge the efficacy of a model. Don’t bother…..Noted …..you assumed normal distribution…. When intercept term is present, R² value evaluates your model wrt. Lower the value, better the model. Then we remove one input feature at a time and train the same model on n-1 input features n times. Answer: This question has enough hints for you to start thinking! What could be a better start for your aspiring career! However, the algorithm of random forest is like a black box. The proportion of 1 (spam) is 70% and 0 (not spam) is 30%. Later, you tried a time series regression model and got higher accuracy than decision tree model. The interviewer really wants to see if you can roll with the punches and if you can make quality decisions when it counts. Ans. Thus, the second statement also comes out to be true. Hence, to avoid these situation, we should tune number of trees using cross validation. 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