How To Explain Machine Learning Model In Interview
How To Explain Machine Learning Model In Interview
In preparation for any interviews, I wanted to share a resource that provides concise explanations of the machine learning model. They are not meant to be extensive, rather the opposite. Hopefully, by reading this, you’ll have a sense of how you can communicate complex models in a simple manner.
Models Covered
- Linear Regression
- Ridge Regression
- Lasso Regression
- Logistic Regression
- K Nearest-Neighbours
- Naive Bayes
- Support Vector Machines
- Decision Trees
- Random Forests
- AdaBoost
- Gradient Boost
- XGBoost
1. Linear Regression
Linear Regression involves finding a ‘line of best fit’ that represents a data set using the least-squares method. The least-squares method involves finding a linear equation that minimizes the sum of squared residuals. A residual is equal to the actual minus predicted value.
To give an example, the red line is a better line of best fit than the green line because it is closer to the points, and thus, the residuals are smaller.
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