Open coderschoolreview opened 6 years ago
Assignment 2
The goal of this assignment was to introduce you to three new classification techniques and to understand how to select the best parameters and features for them. You learned how to use python built-in functions (GridSearchCV, SelectKBest, RFE, SelectFromModel) to try out new models (Support Vector Machines, Random Forests, and Logistic Regression) and test different permutations of parameter values and features, and analyze your results to help build better machine learning models.
Goo job!
Here's what you did well:
Here's some things to keep an eye out on:
SelectKBest
/ SelectFromModel
etc. (i.e. when trying all the parameter optimization methods). That way you have a more fair comparison between the confusion matrices of your classifiers, otherwise it's always going to be likely that your optimized classifier will perform worse than the original and the best.multi_class
value was output as ovr
, but you used multinomial
). In addition, your accuracy did not go down, but you stated that it did.Overall, solid work. Keep it up !!!
The goal of this assignment was to introduce you to 2 main concepts in Machine Learning: Data Pre-processing, and Classification. You learned how to query and clean data using the pandas library in Python, and built a simple Machine Learning Classifier based on the K Nearest Neighbors algorithm.
Things look overall pretty good. There are nicer ways to format the data for the grader (the confusion_matrix output is quite hard to read) but it looks like you just achieved 90%.
In the future, you should try some optional requirements! Does balancing the dataset help improve results?