Related Questions

Why is it essential to consider "model fairness" and "algorithmic bias" when deploying machine learning models in a capstone project?
A company has build a kNN classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong? Note: Model has successfully deployed and no technical issues are found at client side except the model performance
Which of the following are correct statement(s) about stacking?
1. A machine learning model is trained on predictions of multiple machine learning models
2. A Logistic regression will definitely work better in the second stage as compared to other classification methods
3. First stage models are trained on full / partial feature space of training data.
Which of the following type of data science question is missing in the figure?
Introduction to Data Science mcq question image
Below are the two ensemble models: 1. E1(M1, M2, M3) and 2. E2(M4, M5, M6) Above, Mx is the individual base models. Which of the following are more likely to choose if following conditions for E1 and E2 are given? E1: Individual Models accuracies are high but models are of the same type or in another term less diverse E2: Individual Models accuracies are high but they are of different types in another term high diverse in nature
In a data science capstone project, why is it essential to maintain a "model performance dashboard"?
Why is "error analysis" an essential component of model evaluation in a data science capstone project?
What does "interpretability" refer to in the context of a machine learning model in a data science capstone project?
Suppose you are using stacking with n different machine learning algorithms with k folds on data. Which of the following is true about one level (m base models + 1 stacker) stacking? Note: Here, we are working on binary classification problem All base models are trained on all features You are using k folds for base models
Regarding bias and variance, which of the following statements are true? (Here 'high' and 'low' are relative to the ideal model.
i. Models which overfit are more likely to have high bias
ii. Models which overfit are more likely to have low bias
iii. Models which overfit are more likely to have high variance
iv. Models which overfit are more likely to have low variance