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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 – 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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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,
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Machine Learning – L1 & L2 Regularization.
Machine Learning – L1 & l2 Regularization Table Of Contents: What Is L1 & L2 Regularization ? How Controlling The Magnitude Of The Model’s Coefficients, Overcome Overfitting ? How Too Large Coefficients More Likely To Fit Random Noise In The Training Set ? What Is Sparsity In The Model ? How The L2 Regularization Handle The Larger Weights ? Explain With Mathematical Example How The Weights Are Getting Zero In L1 Normalization ? Why For L2 Regularization Weight Can’t Be Zero Explain With One Example ? (1) What Is L1 & L2 Regularization ? (2) How Controlling The Magnitude Of
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What Is Hierarchical Representations In Deep Learning?
What Is Hierarchical Representations In Deep Learning? Table Of Contents: What Is Hierarchical Feature Representation? Key Concepts of Hierarchical Representations. (1) What Is Hierarchical Feature Representation? Hierarchical representations refer to the layered structure of features or patterns that a machine learning model, particularly in deep learning, learns from input data. These representations progress from simple, low-level features in early layers to more complex, high-level abstractions in deeper layers of a neural network. (2) Key Concepts In Hierarchical Representation. (3) Benefits Of Hierarchical Representation.
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Probability Theory
Probability Theory Table Of Contents: Probability Of ‘A’ and ‘B’ Happening Together. Probability Of ‘A’ Given ‘B’ has already Happened. (1) Probability Of ‘A’ and ‘B’ Happening Together. P(A∩B)=P(A)×P(B) for an independent event if there is no relationship between a and b how we are multiplying there individual probability As here we are considering two events we need to consider all possible outcomes for both the events. For rolling two dies together we will have 36 number of outcomes. For tossing two coins together we will hae 4 possible outcomes. (2) Probability Of ‘A’ Given ‘B’ Has Already Happened. (3)
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Parametric & Non Parametric Models
Parametric Vs Non Parametric Models Table Of Contents: Parametric Models Key Characteristics Examples Advantages Disadvantages Non Parametric Models Key Characteristics Examples Advantages Disadvantages Parametric Model: A parametric model assumes a specific functional form for the relationship between the input features and the output. These models have a fixed number of parameters that are determined during the training process. Key Features – Parametric Model Examples – Parametric Model Advantages & Disadvantages – Parametric Model Non – Parametric Model: A non-parametric model makes no strong assumptions about the form of the mapping function. Instead, it learns the structure directly from the data,
