- Explain the working of a Random Forest Machine Learning Algorithm.
- Describe K-Means Clustering.
- How do you parallelize machine learning algorithms?
- How is logistic regression done?
- How do you build a random forest model?
- How can you avoid overfitting your model?
- How do you find RMSE and MSE in a linear regression model?
- After studying the behavior of a population, you have identified four specific individual types that are valuable to your study. You would like to find all users who are most similar to each individual type. Which algorithm is most appropriate for this study?
- What is the goal of A/B Testing?
- Which is your favorite machine learning algorithm and why?
- Have you ever created an original algorithm? How did you go about doing that and for what purpose?
- What is the law of large numbers?
- What are the confounding variables?
- What is selection bias?
- What are the types of biases that can occur during sampling?
- What is survivorship bias?
- Difference between Point Estimates and Confidence Interval
- How can outliers be treated?
- Write a basic SQL query that lists all orders with customer information.
- You are given a dataset on cancer detection. You have built a classification model and achieved an accuracy of 96 percent. Why shouldn't you be happy with your model performance? What can you do about it?
- We want to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate algorithm for this case?
原题：How do you build a random forest model?
2.在所选出来的k个特征中，使用最佳分割点（best split point）计算节点D