Decision tree algorithm interview questions
WebApr 22, 2024 · Which one of the following statements is TRUE for a Decision Tree? (A) Decision tree is only suitable for the classification problem statement. (B) In a decision tree, the entropy of a node … WebOct 7, 2024 · Do not worry, let’s get to those very questions straightaway! What is a random forest? The random forest is a supervised learning algorithm in Machine Learning. It is called random since the data samples it creates for making the decision trees are randomly selected (a form of bagging).
Decision tree algorithm interview questions
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WebDecision tree interview questions are a popular topic for a job seeker preparing for software engineer or web developer interviews. The decision tree algorithm solves many problems in computer programming, so … WebAns:-C50 and tree packages can be used to implement a decision tree algorithm in R. 58. What is Random Forest? Ans:-Random Forest is an Ensemble Classifer. As opposed to building a single decision tree, random forest builds many decision trees and combines the output of all the decision trees to give a stable output. 59.
WebApr 11, 2024 · Answer: A decision tree is a supervised learning algorithm used for classification and regression tasks. It involves recursively splitting the data into subsets … WebWhich argument we need to pass in decision tree to make the algorithm boosting algorithm? Which nodes have the maximum Gini impurity in a decision tree? In decision tree we only use discrete data ? Which …
WebNov 20, 2024 · To which kind of problems are decision trees most suitable? Decision trees are most suitable for tabular data. The outputs are discrete. Explanations for decisions are required. The training data may contain errors. The training data may contain missing attribute values. On what basis is an attribute selected in the decision tree for choosing ... WebAlgorithm Used. Step:1 Choose the best attribute using Attribute Selection Measures (ASM) to divide the records into sub-records. Step:2 Make that current node to a decision node and split the dataset into smaller subsets. Step:3 Build Decision tree until by repeating this process recursively for each child until one of the below condition will ...
WebNov 20, 2024 · 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 …
WebApr 8, 2024 · Here are some of the important Data Science interview questions for freshers: 1. Explain the building of a random forest model. When the data is split into groups, each set makes a decision tree. The role of a random forest model is to get the trees from different groups of data and combine them all. The following are the steps to build a ... indigenous graphic novels canadaWebMar 9, 2024 · Here's a list of the most popular data science interview questions on the technical concept which you can expect to face, and how to frame your answers. 1. What are the differences between supervised and unsupervised learning? Your Data Science Career is Just 6 Months Away! Caltech Data Science Bootcamp Explore Now 2. indigenous government traineeshipWebApr 11, 2024 · Answer: A decision tree is a supervised learning algorithm used for classification and regression tasks. It involves recursively splitting the data into subsets based on the values of the input variables. Advantages of decision trees include their interpretability, ability to handle both categorical and continuous variables, and their … locksmith saddle brook njWebNov 11, 2024 · 7. Top 15 Websites for Coding Challenges and Competitions. 8. 9. Maximize cost to reach the bottom-most row from top-left and top-right corner of given matrix. 10. … locksmith salem oregon heavy equipmentWebA decision tree is a tree-like graph with nodes representing the place where we pick an attribute and ask a question; edges represent the answers the to the question; and the … locksmith san fernando valleyWebOct 27, 2024 · It is one of the trendiest tree questions. Binary trees are used for classification purposes. A decision tree represents a supervised machine-learning algorithm. The binary tree data structure is used to imitate the decision-making process. Usually, a decision tree starts with a root node. The internal nodes are dataset features … indigenous group in central cordilleraWebSep 16, 2016 · You start with the decision tree algorithm, since you know it works fairly well on all kinds of data. Later, you tried a time series regression model and got higher accuracy than decision tree model. Can this happen? Why? Answer: Time series data is known to posses linearity. locksmith salem or