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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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How decision tree works for a regression problem?

Tree works for a regression problem

On this page
  • When decision

When decision

When decision tree algorithm is applied to regression problems, the average of leaf nodes is taken to arrive at the results.

Posted in Decision Tree, Machine Learning, Regression
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