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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
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Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Top 10 Must Read Interview Questions on Decision Trees

Parth Shukla
Parth Shukla Last Updated : 03 Feb, 2023
5 min read

Introduction

Decision trees are one of the most widely used algorithms in machine learning which provide accurate and reliable results that can be used for classification and regression problems. In data science interviews, questions are mostly asked related to decision trees. It is essential to answer those questions efficiently as it is one of the most useful, necessary, and basic algorithms.

This article will discuss the top questions related in machine learning interviews and their appropriate solutions. This will help one to answer those questions efficiently and appropriately.

Learning Objectives

After going through this article, you will learn:

  1. The terminologies CART and ID3 algorithms.
  2. Understanding of entropy and information gain.
  3. The properties of decision trees.
  4. Overfitting and underfitting.
  5.  A proper way to answer interview questions related to the decision tree.
  6. To design and deliver an appropriate answer to interview questions efficiently.

This article was published as a part of the Data Science Blogathon.

Table of Content

  1. What are CART algorithms in machine learning?
  2. What are ID3 algorithms in decision trees?
  3. What is entropy in decision trees?
  4. What is Information gained in decision trees?
  5. What is gini entropy in decision trees?
  6. What are root and leaf nodes in decision trees?
  7. What are shallow and deep decision trees?
  8. How do underfitting and overfitting occur?
  9. Why are decision trees unstable?
  10. Why is feature scaling not required in decision trees?
  11. Conclusion

Q1. What are CART Algorithms in Machine Learning?

CART stands for Classification and Regression Trees, an algorithm that splits the data from the top to the bottom level based on information gain and entropy. This algorithm calculates the impurity for each possible split and then decides according to the minimum loss.

Q2. What are ID3 Algorithms in Decision Trees?

ID3 algorithms are a type of algorithm that works on the principle of impurity, and information gain of the split, the algorithm calculates the entropy and information gain for all splits and divides the data accordingly.

DECISION TREES
DECISION TREES
DECISION TREES

Here note that the CART algorithm is used for binary trees, for example, yes or no. At the same time, the ID3 algorithm is used for multiple class nodes where the tree has more than two leaf nodes.

Q3. What is Entropy in Decision Trees?

Entropy is a measure of uncertainty or impurity that a particular split or node has. Here the data is divided into many divisions, where it can have some impurity. If the data is wholly split homogeneously, the entropy will be zero.

Entropy = -yi(log(yi))

Q4. What is Information Gain?

As the name suggests, the information gained in any decision tree node or split is the information that a particular split provides to the final model. According to the knowledge gained from the split, the last split is a decision on a specific node. The more information gained, the more the chance of implementing the particular split.Information Gain = E(Parent Node) – Weighted Entropy(Child Node)

Q5. What is Gini Entropy?

Gini entropy is also a type of entropy used while splitting the dataset and its nodes. Here the gini entropy is slightly different from the classical entropy, and the calculation of the same is less complex than classical entropy.Gini Entropy – 1 – ((P1)^2 – (P2)^2)

As we can see in the formula calculating the gain entropy for any splish is very easy as we need to calculate the squares of the class and then substitute it, whereas in classical entropy, calculating the log of the classes makes the calculation more complex. Hence, the gini entropy is preferred most for the decision tree algorithm.

Q6. What are Root and Leaf Nodes in Decision Trees?

Root nodes in decision trees are the nodes or points from where the tree starts or where the dataset splits. It is the topmost level of the tree.

DECISION TREES

Whereas the leaf nodes are that nodes are subparts of splits; in simple words, if the root node is split into two parts, then that particular two parts are known as the lead nodes of the particular root node.

Q7. What are Shallow and Deep Decision Trees?

As the name suggests, shallow decision trees are the type of decision trees that are not grown for a very high level of depth and has significantly fewer splits from the data. For example, the decision tree has only three splits.The deep decision trees are grown to their maximum depth and have many splishes in the dataset. For example, the decision tree grows to max depth having 15 or 25 splits.

Q8. How do Overfitting and Underfitting Occur?

In the case of very shallow trees, the tree will have only 2 or 3 splits in the data, meaning that the algorithms will not learn something valuable from the data as there is no sufficient splish in the data. The algorithm will result in poor performing model in training and testing both data, which finally results in the underfitting of the model.

OVERFITTING AND UNDERFITTING

Whereas if we consider a tree grown to its max depth, the tree will know every pattern of the data, and the tree will memorize the information from the data, which will not try to learn from the data and will give 100% accuracy in the training set and poor performance in the testing set, finally resulting in overfitting of the model.

Q9. Why are Decision Trees Unstable Algorithms?

Decision trees are known as unstable algorithms, as when we add a new data point or new data observation to the existing data, due to the unique data point, the model will need to re-train and add the latest data point in the dataset, here the complete tree structure will now of no use and the new tree be required to include that datapoint in the model. Due to this property of the algorithm, they are known as unstable models.

Q10. Why is Feature Scaling not Required in Decision Trees?

The decision trees work on splitting the data according to the information gain and entropy from the split. Here the scale of the data is different from the other attributes; it will not affect the entropy and information gain of the split. Hence, it is not necessary to scale of data before applying the said algorithm.

Conclusion

In this article, we discussed the top interview question that can be asked in data science questions with their appropriate answers. This article will help one to answer these questions efficiently in the interviews and learn a better approach to designing and delivering an accurate and precise response.

Some of the Key Takeaways from this article are:

  1. Decision trees are tree-based models which splits the data and result output.

  2. CART algorithms,s are binary node algorithms, whereas ID3 are multiple node algorithms that can be used for nodes having more than two splits.

  3. Very shallow trees result in underfitting, and intense trees result in overfitting.

  4. Decision trees are unstable algorithm that trains when a new observation is added.

  5. Features scaling is not required for decision trees as it does not affect the information gain and entropy of the split.

 The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Parth Shukla
Parth Shukla

UG (PE) @PDEU | 50+ Published Articles on Data Science | Technical Writer (AI/ML/DL) | Data Science Freelancer | Amazon ML Summer School '22 | Reach Out @[email protected], @portfolio.parthshukla.live

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