Data Science – How XGBoost Algorithm Works ?
(1) Difference Between Training & Testing Set?
(2) Difference In Validation Set & Testing Set?
(3) Define Bias & Variance.
(4) How You Will Handle Missing Values In The Dataset ?
- Mean, Median, Mode
- KNN Imputation, MICE Imputation, Regression Imputation.
- Forward Fill, Backward Fill, Interpolation.
(5) How Decision Tree Classifier Works ?
(6) How Logistic Regression Model Evaluated?
(7) Assumptions Of Linear Regression Model.
- Linearity.
- Multicollinearity.
- Normality.
- Homoscedasticity.
- No Autocorrelation.
(8) What Is Multicollinearity How To Handle It?
(9) Explain Why Performance Of XGBoost Is Better & Why ?
(10) Why Is An Encoder & Decoder Model Is Used In NLP ?
(11) Difference In Machine Learning & Artificial Intelligence .
(12) Difference In Deep Learning & Machine Learning.
(13) What Is Cross Validation ?
(14) What Are The Types Of Machine Learning ?
(15) Difference Between Supervised & Unsupervised Machine Learning ?
(16) What Is Selection Bias ?
(17) What Is The Difference Between The Correlation & Causality ?
(18) What Is The Difference Between Correlation & Covariance ?
(19) What Is The Difference Between Variance & Covariance ?
(20) What Is The Difference Between Supervised & Reinforcement Learning ?
(21) What Are The Requirements Of Reinforcement Learning Environment ?
(22) What Different Targets Do Classification & Regression Algorithm Requires ?
(23) What Five Popular Algorithms Used In Machine Learning ?
(24) What Is Confusion Matrix ?
(25) List The Difference Between KNN & K – Means Clustering .
(26) What Are Difference Between Type-1 & Type – 2 Error ?
(27) What Is Semi Supervised Learning ?
(28) What Is Semi Supervised Learning ?
(29) What Is Stemming ?
(30) What Is Lemmatization ?
(31) What Is A PCA ?
(32) What Are Support Vectors In SVM ?
(33) In terms Of Access How Arrays & Linked Lists Are Different ?
(33) What Is P – Value ?
(34) What Techniques Are Used To Find Resemblance In The Recommendation System ?
(35) What Is A ROC Curve ?
(35) What Does Area Under ROC Curve Indicate ?
(36) What Is An Outlier ?
(37) What Are The Outlier Handling Techniques ?
(38) What Is Another Name Of The Bayesian Network ?
(39) What Is Ensemble Learning ?
(40) What Is Clustering ?
(41) How Would You Define Collinearity ?
(42) What Is The Bayesian Network ?
(43) What Is The Time Series ?
(44) What Is The Dimension Reduction In ML ?
(45) What Is Underfitting ?
(46) What Is Sensitivity ?
(47) What Is Specificity ?
(48) Batch, Mini-batch & Stochastic Gradient Descent .
(49) Why Is Naive Bayes Method Is ‘Naive’ ?
(50) State The Bayes Theorem For Naive Bayes Algorithm.
(51) What Are Some Tools Used To Discover Outliers ?
(52) Explain Kernel In SVM ?
(53) What Are Different Types Of Clustering Algorithms ?
(54) How Would You Describe Reinforcement Learning ?
(55) What Is Context Based Filtering & Collaborative Filtering ?
(56) What Is Deductive Learning & Inductive Learning ?
(57) How Do You Differentiate Data Mining Vs. Machine Learning ?
(57) Why ROC Curve Is Important ?
(58) Why Does Overfitting Occurs In ML ?
(59) What Are Some Functions Of Unsupervised Learning ?
(60) What Are Some Functions Of Unsupervised Learning ?
(61) What Are All Components Of Bayesian Logic ?
(62) How Would You Describe A Recommender System ?
(63) What Is Regularization In ML?
(64) Advantages & Disadvantages Of Decision Tree ?
(65) What Do You Understand About Exploding Gradient Problem In Machine Learning ?
(66) How To Detect Exploding Gradient Problem Neural Network ?
(67) How To Handle Exploding Gradient Problem In Neural Network ?
(68) What Is Vanishing Gradient Problem In Neural Network ?
(69) How To Detect Vanishing Gradient Problem ?
(70) How To Handle Vanishing Gradient Problem ?
(71) Difference Between Standardization & Normalization ?
(72) How Would You Describe F1 Score And How Would You Use It ?
(73) Explain The Difference Between Loss Function & Cost Function ?
(74) How To Handle Outliers ?
(75) What Is A Random Forest & How Does It Works ?
(76) What Methods Can Be Used To Find The Threshold Of A Classifier ?
(77) How Can You Check Normality Of A Dataset ?
(78) How Can You Differentiate Between A Parametric & Non Parametric Model ?
(79) How Can Logistic Regression Can Be Used For More Than One Class ?
(80) What Difference Exists Between Softmax & Sigmoid Functions ?
(81) Which Is Better To Have A False Positive Or False Negative ?
(82) How Would You Handle A Dataset Suffering From High Variance ?
(83) How Regularization Reduces The Cost Term ?
(84) Difference Between Gradient Boosting & Random Forest ?
(85) How Does Box -Cox Transformation Occur ?
