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Ai, Ml ... docs
  • Regression
    • Regression ?
    • Residuals
    • Multicollinearity
    Linear Regression
    • Linear Regression?
    • Use Linear Regression? Explain the equation of a straight line.
    • While using linear regression, what kind of plots will you use to showcase the relationship amongst the columns?
    • In linear regression, what are the steps taken to arrive at the bestfit line?
    • Gradient descent, and why is it used?
    Machine Learning
    • correlation?
    • The null and alternate hypothesis?
    • Regularization needed?
    • Abstraction?
    • Normalization?
    • What does the term ‘Generalization’ signify in machine learning?
    • The machine learning model said to suffer from generalization failure?
    • Measure generalization performance?
    • Detect multicollinearity?
    • The remedies for multicollinearity?
    • What are Bias and Variance? What is Bias Variance Trade-off?
    • Elastic Net.
    • Why do we do a train test split?
    • Describe polynomial regression in few words
    • Goodness of Fit?
    • Improve generalization performance?
    • R-Squared Statistics?
    • Adjusted R-Squared Statistics?
    • Why do we use adjusted R-squared?
    • Why adjusted R-squared decreases when we use incompetent variables?
    • Interpret a Linear Regression model?
    • Plot the least squared line?
    • Multiple linear regression?
    • Handle categorical values in the data?
    • Explain Lasso Regression.
    Logistic Regression, Machine Learning
    • Classification problem?
    • Enumerate the difference between classification and clustering problems
    • Logistic Regression?
    • Sigmoid function used for Logistic Regression?
    • Which attributes of sigmoid function make it a suitable candidate for logistic regression algorithm?
    • Cost function?
    • Multiple Logistic Regression?
    • Multiclass Logistic Regression?
    • Enumerate the methods applied in multi class Logistic Regression.
    • Multi class Logistic Regression?
    • How does the Logistic Regression Algorithm learn?
    • Cost function computed in Logistic Regression?
    • AUC, and when is it used?
    • Explain the confusion matrix
    • Accuracy?
    • Why there is a need for other metrics if ‘accuracy’ is already present?
    • Recall and Precision? How do they differ?
    • Precision?
    • Recall?
    • Tradeoff between recall and precision work?
    • F1 score?
    • How does the tradeoff between recall and precision work?
    • Specificity?
    • Explain the significance of ROC.
    • AUC, and when is it used?
    Decision Tree
    • Decision tree works for a regression problem?
    • Recursive binary splitting is called Greedy Approach?
    • Pre pruning and post pruning?
    • What is Entropy? How is it calculated?
    • Gini Impurity?
    • What do you understand by Information Gain? How does it help in tree building?
    • How does node selection take place while building a tree?
    • What are different algorithms available for Decision Tree?
    • The disadvantages and advantages of using a Decision Tree?
    • Cross validation?
    • What are different types of CV methods?
    • How bias and variance varies for each CV method?
    Ensemble Technique
    • What do you understand by Greedy approach?
    • Pruning?
    • Explain the idea behind ensemble techniques.
    • What is Bootstrapping? How is sampling done in bootstrapping?
    Bagging
    • Bagging?
    • How prediction is made in Bagging?
    • How Ensemble technique solves the high variance issue with Decision trees?
    • Out Of Bag evaluation?
    • How does a Random Forest model works?
    • What is the difference between Bagging and Random forest? Why do we use Random forest more commonly than Bagging?
    • What is pasting? How is it different from bagging?
    Clustering, Unsupervised Learning
    • Clustering?
    • What are the various applications of clustering?
    • What are the requirements to be met by a clustering algorithm?
    • Discuss the different approaches for clustering
    • Discuss the elbow method.
    • Discuss the step by step implementation of K-Means Clustering.
    • The challenges with K-Means?
    • Discuss the agglomerative and divisive clustering approaches.
    • Dendrograms?
    • Discuss the Hierarchical clustering in detail.
    • Discuss the various linkage methods for clustering.
    • Discuss the differences between K-Means and Hierarchical clustering.
    • Discuss the various improvements in K-Means
    Misc....
    • Feature sampling?
    • How prediction is made in Random Forest?
    • Explain the working behind Stacking.
    • Stacking done?
    • Stacking different from bagging?
    • Boosting?
    • How do boosting and bagging differ?
    • Weak and strong classifiers?
    • Pseudo residuals?
    • Explain the step by step implementation of ADA Boost.
    • Explain the step by step implementation of Gradient Boosted Trees.
    • XGBoost so popular?
    • Explain the step by step implementation of XGBoost Algorithm.
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What is Regression?

Regression

WHat is regression?

Posted in Regression
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