What are the underlying assumptions of Logistic Regression?
- Linearity of predictors and log-odds, Independence of errors, No multicollinearity
- Linearity, Independence, Normality, Equal Variance
- No assumptions required
- Nonlinearity, Dependence, Non-Normality
Logistic Regression assumes a linear relationship between predictors and log-odds, independence of errors, and no multicollinearity among predictors. It does not assume normality or equal variance of errors.
The ___________ test in Logistic Regression can be used to assess if the Logit link function is the correct specification for the model.
- AIC
- Hosmer-Lemeshow
- Likelihood-ratio
- Link
The Link test in Logistic Regression can be used to determine if the Logit link function is the correct specification for the model.
Which field utilizes Machine Learning to recommend products or media to consumers based on their past behavior?
- Autonomous Driving
- Education
- Healthcare
- Recommender Systems
Recommender Systems use machine learning algorithms to suggest products, media, or content to users based on their past interactions and behavior, creating personalized experiences.
You built a regression model and it's yielding a very low R-Squared value. What could be the reason and how would you improve it?
- Data noise; Apply data cleaning
- Incorrect model; Change the model
- Poorly fitted; Improve the model fit
- Too many features; Reduce features
A low R-Squared value might indicate that the model doesn't fit the data well. This could be due to an incorrect choice of model, underfitting, or other issues. Improving the model fit by selecting an appropriate algorithm, feature engineering, or hyperparameter tuning can address this problem.
What is Bootstrapping, and how does it differ from Cross-Validation?
- A method for resampling data with replacement
- A technique for training ensemble models
- A technique to reduce bias
- A type of Cross-Validation
Bootstrapping is a method for resampling data with replacement, used to estimate statistics about a population from a sample. It differs from Cross-Validation, where data is split without replacement to validate the model. Bootstrapping is more about estimating the properties of an estimator, while Cross-Validation assesses the model's performance.
What are the main challenges in training a Machine Learning model with imbalanced datasets?
- Computational complexity
- Dimensionality reduction
- Lack of suitable algorithms
- Overfitting to the majority class
Training on imbalanced datasets can lead to models that are biased towards the majority class, since they have seen more examples of it. This can make the model perform poorly on the minority class.
While estimating the coefficients in Simple Linear Regression, you find that one of the assumptions is not met. How would this affect the reliability of the predictions?
- Increase Accuracy
- Make Predictions More Reliable
- Make Predictions Unreliable
- No Effect
If the assumptions of Simple Linear Regression are not met, the reliability of the predictions may be compromised, and the model may become biased or inefficient.
What are the underlying assumptions of LDA, and how do they affect the performance?
- Assumes different covariance matrices, normal distribution; affects adaptability
- Assumes equal class sizes; affects bias
- Assumes equal variance, non-normal distribution; affects robustness
- Assumes normal distribution, equal covariance matrices; affects classification accuracy
LDA assumes that the features are normally distributed and that the classes have equal covariance matrices. These assumptions, if met, lead to better "classification accuracy," but if violated, may lead to suboptimal performance.
__________ is a boosting algorithm that builds trees one at a time, where each tree corrects the errors of the previous one.
- AdaBoost
- Bagging
- Gradient Boosting
- Random Forest
AdaBoost is a boosting algorithm that builds trees sequentially, with each tree correcting the errors of the previous one by giving more weight to the misclassified instances.
What is the relationship between dimensionality reduction and the curse of dimensionality?
- Depends on the method
- Has no relationship
- Helps in mitigating the curse
- Intensifies the curse
The relationship between dimensionality reduction and the curse of dimensionality is that dimensionality reduction helps in mitigating the curse. The curse of dimensionality refers to the challenges that arise as the number of dimensions increases, and reducing dimensions can alleviate these problems.