In both random forest and gradient boosting, real values can be handled by making them discrete. Assuming the role for which you are hiring an employee involves decision making, listen for past actions that demonstrate that the applicant can make logical, realistic decisions. Since the information which is fed into each tree comes out to be unique, the likelihood of any tree having any impact on the other becomes very low. Also, adding correlated variables lets PCA put more importance on those variable, which is misleading. Algorithm of bagging works best for the models which have high variance and low bias? Why? Shouldn’t organizations recruiting specify their specialty requirements too? The decision trees shown to date have only one decision point. Ans. Example: Think of a chess board, the movement made by a bishop or a rook is calculated by manhattan distance because of their respective vertical & horizontal movements. It is an indicator of percent of variance in a predictor which cannot be accounted by other predictors. Ans. Ordinary least square(OLS) is a method used in linear regression which approximates the parameters resulting in minimum distance between actual and predicted values. You are working on a time series data set. This is how a machine works & develops intuition from its environment. It is also known as lazy learner because it involves minimal training of model. kNN algorithm tries to classify an unlabeled observation based on its k (can be any number ) surrounding neighbors. The correct answer to this question is C because, for a bagging tree, both of these statements are true. This question is straightforward. Following are these component : Bias error is useful to quantify how much on an average are the predicted values different from the actual value. They both can easily handle the features which have real values in them. Information gain ratio biases the decision tree against considering attributes with a large number of distinct values which might lead to overfitting. I was wondering, do you recommend for somebody to special in a specific field of ML? 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. Covariances are difficult to compare. It has dimension restrictions. Q6. To help you prepare for your next interview, I’ve prepared a list of 40 plausible & tricky questions which are likely to come across your way in interviews. Looking for more sample interview questions? Each tree which constitutes the random forest is based on the subset of all the features. Answer: Tolerance (1 / VIF) is used as an indicator of multicollinearity. Since, the data is spread across median, let’s assume it’s a normal distribution. Answer: The basic idea for this kind of recommendation engine comes from collaborative filtering. Type II error occurs when we classify a value as negative (0) when it is actually positive(1). For example: The probability that the word ‘FREE’ is used in previous spam message is likelihood. On the other hand, a decision tree algorithm is known to work best to detect non – linear interactions. 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. If an attribute is often selected as best split, it is most likely an informative feature to retain. If we are to increase this hyperparameter’s value, then the chances of this model actually underfitting the data increases. On the other hand, GBM improves accuracy my reducing both bias and variance in a model. In this technique,  a model is usually given a dataset of a known data on which training (training data set) is run and a dataset of unknown data against which the model is tested. Ans. But if you have a small database and you are forced to come with a model based on that. How do you decide a feature suitability when working with decision tree? 30) Why instance based learning algorithm sometimes referred as Lazy learning algorithm? Using online learning algorithms like Vowpal Wabbit (available in Python) is a possible option. Answer: You can quote ISLR’s authors Hastie, Tibshirani who asserted that, in presence of few variables with medium / large sized effect, use lasso regression. If the decisions really seem illogical, like unsupported leaps of faith, or to come from out of the left-field, though, be wary of the candidate. The two techniques of Machine Learning are. 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. The problem with correlated models is, all the models provide same information. All rights reserved, However, that does not mean that you will not be able to understand what the tree is doing at each node. When p > n, we can no longer calculate a unique least square coefficient estimate, the variances become infinite, so OLS cannot be used at all. I mean, it is recommended to choose between supervised learning and unsupervised learning algorithms, and simply say my specialty is this during an interview. 2. In such situation, you can use a technique known as cross validation. Haven’t you trained your model perfectly? In every stage of boosting, the algorithm introduces another tree to ensure all the current model issues are compensated. You will still be able to interpret what is happening even after you implement the algorithm of Random Forest. Answer: After reading this question, you should have understood that this is a classic case of “causation and correlation”. Explain your thought process. 9) What are the three stages to build the hypotheses or model in machine learning? What's important to you in making this decision. Hence, it doesn’t use training data to make generalization on unseen data set. Hence, it tries to push the coefficients for many variables to zero and hence reduce cost term. Thank you Manish.Helpful for Beginners like me. You can get that know-how in our course ‘Introduction to Data Science‘! Decision Tree Questions To Ace Your Next Data Science Interview. 3) What is ‘Overfitting’ in Machine learning? Only statement number one and four is TRUE. We start with 1 feature only, progressively adding 1 feature at a time, i.e. In simple words, the tree algorithm find the best possible feature which can divide the data set into purest possible children nodes. You have built a multiple regression model. After The Interview Ends: What to Do One Hour, Five Days, and Two Weeks Later, Describe an Important Project You Worked On. This trait is particularly important in business context when it comes to explaining a decision to stakeholders. Only Extra Trees and Random forest does not have a learning rate as one of their tunable hyperparameters. So, the answer would be g because the statement number one and three are TRUE. Answer: Yes, rotation (orthogonal) is necessary because it maximizes the difference between variance captured by the component. Hi Amit, The expected error of a learning algorithm can be decomposed into bias and variance. The final result which all these trees give is collected and then processed to provide the output. Q17. https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff. Gini index says, if we select two items from a population at random then they must be of same class and probability for this is 1 if population is pure. 1.Missing Values Ratio How ? Answer: Regularization becomes necessary when the model begins to ovefit / underfit. ‘People who bought this, also bought…’ recommendations seen on amazon is a result of which algorithm? I Have small suggestion on Dimensionality Reduction,We can also use the below mentioned techniques to reduce the dimension of the data. Great article. In absence of intercept term (ymean), the model can make no such evaluation, with large denominator, ∑(y - y´)²/∑(y)² equation’s value becomes smaller than actual, resulting in higher R². So, the answer would be g because the statement number one and three are TRUE. If you keep on increasing the value of this hyperparameter, then the model is bound to overfit. 14) Explain what is the function of ‘Unsupervised Learning’? The way to look at these questions is to imagine each decision point as of a separate decision tree. Here we calculate the correlation coefficient between numerical columns and between nominal columns as the Pearson’s Product Moment Coefficient and the Pearson’s chi square value respectively. How would you check if he’s true? You cannot solve it mathematically (even by writing exponential equations).

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