decision tree interview questions

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Is rotation necessary in PCA? Conceptually, we can say, lasso regression (L1) does both variable selection and parameter shrinkage, whereas Ridge regression only does parameter shrinkage and end up including all the coefficients in the model. The mechanism of creating a bagging tree is that with replacement, a number of subsets are taken from the sample present for training the data. It’s just like how babies learn to walk. Answer: The basic idea for this kind of recommendation engine comes from collaborative filtering. On the contrary, stratified sampling helps to maintain the distribution of target variable in the resultant distributed samples also. You are given a data set. 12) List down various approaches for machine learning? These methods are designed for binary classification, and it is not trivial. Susan Heathfield is an HR and management consultant with an MS degree. One caveat with these guidelines, however, is that you want to hire people who are creative, innovative, and willing to step outside of the box. Check out the ‘Ace Data Science Interviews‘ course taught by Kunal Jain and Pranav Dar. The contextual question is, which of the following would be true in the paradigm of ensemble learning. Both of these ensemble methods are actually very capable of doing both classification and regression tasks. Good collection compiled by you Mr Manish ! Following are the methods you can use to tackle such situation: Note: For point 4 & 5, make sure you read about online learning algorithms & Stochastic Gradient Descent. Answer: Following are the methods of variable selection you can use: Q19. This is the official account of the Analytics Vidhya team. This is the inverse process to the Backward Feature Elimination. 40) What is dimension reduction in Machine Learning? Additionally, knowing more about the company will help you tailor your responses to what the hiring manger wants to hear. A machine learning problem consist of three things: Always look for these three factors to decide if machine learning is a tool to solve a particular problem. During the interview, listen for evidence of a systematic approach to weighing options. For categorical variables, we’ll use chi-square test. What do you understand by Bias Variance trade off? Using online learning algorithms like Vowpal Wabbit (available in Python) is a possible option. In bagging technique, a data set is divided into n samples using randomized sampling. Random forest improves model accuracy by reducing variance (mainly). In bagging trees or bootstrap aggregation, the main goal of applying this algorithm is to reduce the amount of variance present in the decision tree. Therefore, ensemble learners are built on the premise of combining weak uncorrelated models to obtain better predictions. In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. It was to calculate from median and not mean. Why? 40 Interview Questions asked at Startups in Machine Learning / Data Science, Q1. You will see four statements listed below. You will have to read both of them carefully and then choose one of the options from the two statements’ options. You can ask these interview questions about decision making to determine his or her experience and competency in making decisions at work. PCA (Principal Components Analysis), KPCA ( Kernel based Principal Component Analysis) and ICA ( Independent Component Analysis) are important feature extraction techniques used for dimensionality reduction. (adsbygoogle = window.adsbygoogle || []).push({}); This article is quite old and you might not get a prompt response from the author. For improvement, your remove the intercept term, your model R² becomes 0.8 from 0.3. The contextual question is, Choose the statements which are true about Radom forests and Gradient boosting ensemble method. Do you know how does a tree splitting takes place i.e. Answer: Correlation is the standardized form of covariance. Contains a list of widely asked interview questions based on machine learning and data science So what will be true about each or any of the trees in the random forest? Answer: Tolerance (1 / VIF) is used as an indicator of multicollinearity. Explain the statement. 8) What are the different Algorithm techniques in Machine Learning? What will be your criteria? Though, ensembled models are known to return high accuracy, but you are unfortunate. Now, each of these smaller subsets of data is used to train a separate decision tree. Answer: We can deal with them in the following ways: 29. A high variance model will over-fit on your training population and perform badly on any observation beyond training. True. The hyperparameter max_depth controls the depth until the gradient boosting will model the presented data in front of it. Shorter trees are preferred over longer trees. To succeed, they even seek support from the door or wall or anything near them, which helps them stand firm. Answer: The fundamental difference is, random forest uses bagging technique to make predictions. Each iteration k produces a model trained on n-k features and an error rate e(k). Therefore, it depends on our model objective. In short, there is no one master algorithm for all situations. Answer: We don’t use manhattan distance because it calculates distance horizontally or vertically only. The different types of techniques in Machine Learning are. Ans. Then, using a single learning algorithm a model is build on all samples. Hence, to avoid these situation, we should tune number of trees using cross validation. Q14. 5. 7) What are the five popular algorithms of Machine Learning? Yes, the gradient descent algorithm is the function that is applied to reduce the loss function. How do you plan to reach your professional goals? Lower the value, better the model. How? The correct answer to this question is C because, for a bagging tree, both of these statements are true. Have also taken note of Karthi’s input! The threshold is decided by maximizing the information gain. Considering the long list of machine learning algorithm, given a data set, how do you decide which one to use? One hot encoding ‘color’ variable will generate three new variables as Color.Red, Color.Blue and Color.Green containing 0 and 1 value. You came to know that your model is suffering from low bias and high variance. What could be a better start for your aspiring career! A word of caution: correlation is scale sensitive; therefore column normalization is required for a meaningful correlation comparison. We start with 1 feature only, progressively adding 1 feature at a time, i.e. A more candid reply will do a better job of conveying your enthusiasm for the industry and the position. Statistical learning techniques allow learning a function or predictor from a set of observed data that can make predictions about unseen or future data. Since we have lower RAM, we should close all other applications in our machine, including the web browser, so that most of the memory can be put 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. Try to solve each of these questions first before reading the solutions to gain the most out of these questions. . A bias term measures how closely the average classifier produced by the learning algorithm matches the target function. The answer to this question is C meaning both of the two options are TRUE. A random sampling doesn’t takes into consideration the proportion of target classes. Dedicated to helping job seekers find work during the pandemic. Q9. In the context of confusion matrix, we can say Type I error occurs when we classify a value as positive (1) when it is actually negative (0). In both random forest and gradient boosting, real values can be handled by making them discrete. Hence, it doesn’t use training data to make generalization on unseen data set. Here Are Tips for How to Answer Firefighter Job Interview Questions, Use Sample Questions to Select the Most Qualified HR Job Applicant, Common Phone Interview Questions and Best Answers, Interview Questions for an Event Planner Position, 12 of the Toughest Interview Questions With Answers, management philosophy of employee empowerment, select the best, most qualified employees. No, we can’t conclude that decrease in number of pirates caused the climate change because there might be other factors (lurking or confounding variables) influencing this phenomenon.

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