decision tree interview questions

Later, the resultant predictions are combined using voting or averaging. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Q17. Considering the long list of machine learning algorithm, given a data set, how do you decide which one to use? After going through these question I feel I am at 10% of knowledge required to pursue career in Data Science . By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small dataset, and you try to learn from it. Do you suggest that treating a categorical variable as continuous variable would result in a better predictive model? MMH is the line which attempts to create greatest separation between two groups. One approach to dimensionality reduction is to generate a large and carefully constructed set of trees against a target attribute and then use each attribute’s usage statistics to find the most informative subset of features. We can alter the prediction threshold value by doing. 12) List down various approaches for machine learning? Designing and developing algorithms according to the behaviours based on empirical data are known as Machine Learning. The data set is based on a classification problem. Use regularization technique, where higher model coefficients get penalized, hence lowering model complexity. 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. Or, we can sensibly check their distribution with the target variable, and if found any pattern we’ll keep those missing values and assign them a new category while removing others. Yes, the gradient descent algorithm is the function that is applied to reduce the loss function. Good to know, you found them helpful! Q22. Why not manhattan distance ? It’s just like how babies learn to walk. It has dimension restrictions. 23) What is Model Selection in Machine Learning? How is kNN different from kmeans clustering? None of the options which are mentioned above. Hi, really an interesting collection of answers. A word of caution: correlation is scale sensitive; therefore column normalization is required for a meaningful correlation comparison. The following are some of the questions which can be asked in the interviews. That was about the structure of the tree; however, the surge in decision trees’ popularity is not due to the way they are created. Briefly describe the situation, your decision and the effects of that decision. Answer: We can deal with them in the following ways: 29. Then we remove one input feature at a time and train the same model on n-1 input features n times. Details Last Updated: 20 October 2020 . 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. Please share the pdf format of this blog post if possible. What would you do? Hi Kavitha, I hope these questions help you to prepare for forthcoming interview rounds. The trees grown are uncorrelated to maximize the decrease in variance. Q14. Therefore, we always prefer model with minimum AIC value. So, you are bound to lose all the interpretability after you apply the random forest algorithm. Following are frequently asked questions in interviews for freshers as well experienced ETL tester and... Log Management Software are tools that deal with a large volume of computer-generated messages. Q1. The correct answer to this question is C because, for a bagging tree, both of these statements are true. How you respond to an inquiry regarding your decision-making skills can set you apart from the other candidates who applied for the position. Thanks a ton Manish sir for the share. The problem with correlated models is, all the models provide same information. In today's job market, hiring managers need to understand potential employees before offering them a position. 16) What is algorithm independent machine learning? Should I become a data scientist (or a business analyst)? If we are to increase this hyperparameter’s value, then the chances of this model actually underfitting the data increases. Also, ridge regression works best in situations where the least square estimates have higher variance. The problem is, company’s delivery team aren’t able to deliver food on time. Answer: OLS and Maximum likelihood are the methods used by the respective regression methods to approximate the unknown parameter (coefficient) value. A Computer Science portal for geeks. The formula of R² = 1 – ∑(y – y´)²/∑(y – ymean)² where y´ is predicted value. Answer: The basic idea for this kind of recommendation engine comes from collaborative filtering. These questions are meant to give you a wide exposure on the types of questions asked at startups in machine learning. You will have to read all of them carefully and then choose one of the options from the options which follows the four statements. As such, they don't want to hear the same prepared answers to the expected questions. Also, we can add some random noise in correlated variable so that the variables become different from each other. Q16. Ans. What’s about it? I’d love to know your experience. We start with 1 feature only, progressively adding 1 feature at a time, i.e. Following are these component : Bias error is useful to quantify how much on an average are the predicted values different from the actual value. The node of any decision tree represents a test done on the attribute. How Soon Should You Follow Up After a Job Interview? This also means that there are numerous exciting startups looking for data scientists. We must be scrupulous enough to understand which algorithm to use. of variable) > n (no. You don't need to ask all of these questions, but if decision making is a responsible component in the job you are filling, you will want to ask several interview questions about your candidate's experience and effectiveness in decision making. Before the interview, think about moments where you really excelled professionally. For example: Robots are programed so that they can perform the task based on data they gather from sensors. In machine learning, thinking of building your expertise in supervised learning would be good, but companies want more than that. In short, there is no one master algorithm for all situations. The contextual question is, which of the following would be true in the paradigm of ensemble learning. A random sampling doesn’t takes into consideration the proportion of target classes. You are working on a classification problem. For categorical variables, we’ll use chi-square test. Think about a time when you had several options from which to choose, but none of them were sufficient to meet your goal. Decision trees are most suitable for tabular data. Bagging is a method in ensemble for improving unstable estimation or classification schemes. 2) Mention the difference between Data Mining and Machine learning? In random forest, it happens when we use larger number of trees than necessary. If you are beginning to advance through the ranks of your industry, you may not have encountered decision making questions before. Q40. Look for evidence of effective decision making in the past. Q25. All the best. The next time they fall down, they feel pain. While artificial intelligence in addition to machine learning, it also covers other aspects like knowledge representation, natural language processing, planning, robotics etc. If a supervisor or co-worker has ever commended you on your decision making, this is likely a great experience to share with the hiring manager. Anyway, memorized stock replies sound rehearsed and may indicate that you are treating this interview as a run-of-the-mill experience. You are confident that your model will work incredibly well on unseen data since your validation accuracy is high. Unfortunately, neither of models could perform better than benchmark score. We can assign weight to classes such that the minority classes gets larger weight. Answer: Following are the methods of variable selection you can use: Q19. This question is straightforward. Can you recount an occasion where you had to choose between equally viable options to accomplish a single goal? I was wondering, do you recommend for somebody to special in a specific field of ML? The correct option will be B, i.e., only the statement number two is TRUE, and the statement number one is FALSE. Model selection is applied to the fields of statistics, machine learning and data mining. On the other hand, GBM improves accuracy my reducing both bias and variance in a model. But, they learn ‘not to stand like that again’. If we have the same scores on the validation data, we generally prefer the model with a lower depth. ... A decision tree is a tree in which every node specifies a test of some attribute of the data and each branch descending from that node corresponds to one of the possible values for this attribute.

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