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Data Science – How To Handle Data Imbalance ?
Data Science – How To Handle Data Imbalance? Table Of Contents: Resampling Techniques. Class Weighting. Algorithmic Approach. Evaluation Metric Adjustments. Data Augmentation. Anomaly Detection Approach. (1) Resampling Techniques Types Of Resampling Techniques: When To Use Which Sampling Techniques ? (2) Class Weight Balance Technique Python Implementation When To Use Class Weight Technique: (3) Algorithmic Approach Of Handling Class Imbalance. (4) Evaluation Metric Adjustments. Can We Use ROC – AUC for Imbalanced Class ? (5) Data Augmentation Techniques to Handle Class Imbalance Common Data Augmentation Techniques for Imbalanced Data
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Data Science – Short Answers !
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
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Data Science – How XGBoost Algorithm Works ?
Data Science – How XGBoost Algorithm Works ? Table Of Contents: What Is XGBoost ? How XGBoost Algorithm Works ? Bonus Concept . Example Of XGBoost . (1) What Is XGBoost Algorithm ? (2) How XGBoost Algorithm Works ? (3) How XGBoost Algorithm Works ? (4) Example Of XGBoost . (5) Why XGBoost Is Popular ?
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Data Science – What Are Attribute Selection Measures In A Decision Tree ?
Data Science – What Are Attribute Selection Measures Used In Decision Tree ? Table Of Contents: Entropy. Information Gain. Gini Index. Gain Ratio. Chi – Square Test. Reduction In Variance. (1) Entropy: (2) Information Gain (3) What Is Gini Index ? Why We Do Square Of Probability ? Why We Do Square Of Probability ? (4) What Is The Issue With Information Gain ? (5) What Is Gain Ratio ? Example:
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Data Science – When Should We Stop Growing Decision Tree ?
Data Science – When Should We Stop Growing Decision Tree ? Table Of Contents: When Should We Stop Growing Decision Tree? (1) When Should We Stop Growing Decision Tree Algorithm ?
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Data Science – What Is Interpolation ?
Data Science – What Is Interpolation ? Table Of Contents: What Is Interpolation ? Assumptions Of Interpolation ? Linear Interpolation. Polynomial Interpolation. Spline Interpolation. (1) What Is Interpolation ? Interpolation is a technique used to estimate or “fill in” missing values in a dataset by using the values of surrounding data points. In other words, it generates a smooth transition between known values by estimating the unknown values in between. This is especially useful in time series or continuous numerical data where missing points can disrupt trends or patterns. (2) Assumptions Of Interpolation ? Interpolation assumes that data points near
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Data Science – How To Handle Missing Values In A Dataset ?
Data Science – How To Handle Missing Values In A Dataset? Table Of Contents: Simple Imputation Techniques. Advanced Statistical Methods. Time Series Specific Imputation. Model-Based And Ensemble Imputation. Domain Specific or Hybrid Approaches. (1) Simple Imputation Techniques (2) Advanced Statistical Methods (3) Time Series Specific Imputation (4) Model Based & Ensemble Imputation (5) Domain Specific & Hybrid Approaches
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Data Science – Interview Q & A .
Data Science – Interview Q & A. Set-1: Difference Between Training & Testing Set? Difference In Validation Set & Testing Set? Define Bias & Variance. How You Will Handle Missing Values In The Dataset ? How Decision Tree Classifier Works ? How Logistic Regression Model Evaluated? Assumptions Of Linear Regression Model. What Is Multicollinearity How To Handle It? Explain Why Performance Of XGBoost Is Better & Why ? Why Is An Encoder & Decoder Model Is Used In NLP ? Set-2: Difference In Machine Learning & Artificial Intelligence ? Difference In Deep Learning & Machine Learning . What Is Cross
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Linear Regression – Interview Question & Answers !
Linear Regression – Interview Q & A. Table Of Contents: Beginner-Level (Fundamentals) What is Linear Regression? What is the equation of a simple linear regression model? What are the assumptions of linear regression? What is the difference between simple and multiple linear regression? What do the coefficients in a linear regression model represent? How do you interpret the intercept and slope in a regression line? What is the cost function used in linear regression? What is the difference between correlation and regression? What is Mean Squared Error (MSE)? How is R-squared interpreted? What does an R-squared of 0.85 mean? What
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Deep Learning – What Is Early Stopping ?
Deep Learning – What Is Early Stopping ? Table Of Contents: What Is Early Stopping ? Why Is Early Stopping Is Needed ? How Early Stopping Works ? Benefits Of Early Stopping . Visual Representation. Hyperparameter : Patience . (1) What Is Early Stopping ? (2) Why Is Early Stopping Needed ? (3) How Early Stopping Works ? (4) Benefits of Early Stopping . (5) Visual Representation . (6) Hyperparameter: Patience (7) Implementation in Keras (TensorFlow) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.callbacks import EarlyStopping # 1. Build the model model = Sequential([ Dense(128, activation='relu', input_shape=(input_dim,)), Dense(64,
