Date: 18 June 2022 – Click here to see LinkedIn Post.
Q1. How to improve your Model performance?
A. Follow these techniques:
1. Use Validation methods
2. Add more data
3. Apply feature engineering techniques(Normalization, Imputation, etc)
4. Compare Multiple algorithms
5. Hyperparameter Tuning
Q2. Difference between Standardization and Log Transformation.
A. Standardization is the process of putting different variables on the same scale. This process allows you to compare scores between different types of variables. Typically, to standardize variables, you calculate the mean and standard deviation for a variable. Log transformation is a data transformation method in which it replaces each variable x with a log(x). Log-transform decreases skew in some distributions, especially with large outliers. But, it may not be useful as well if the original distribution is not skewed. Also, log transform may not be applied to some cases (negative values), but standardization is always applicable (except σ=0).
Q3. How to deal with unbalanced binary classification?
A. In a classification task, there is a high chance for the algorithm to be biased if the dataset is imbalanced. An imbalanced dataset is one in which the number of samples in one class is very higher or lesser than the number of samples in the other class.
To counter such imbalanced datasets, we use a technique called up-sampling and down-sampling.
In up-sampling, we randomly duplicate the observations from the minority class in order to reinforce its signal. The most common way is to resample with replacement. In down-sampling, we randomly remove the observations from the majority class. Thus after up-sampling or down-sampling, the dataset becomes balanced with the same number of observations in each class.
Q4. Difference between Precision and Recall.
A. The recall is the ratio of the relevant results returned by the search engine to the total number of the relevant results that could have been returned. The precision is the proportion of relevant results in the list of all returned search results.
Q5. What is the bias-variance trade-off?
A. The bias-variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.
Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science & Machine Learning course from “Learn Everything AI”. This comprehensive and project-based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real-world projects to add to your portfolio.
Become a complete Data Scientist and Machine Learning engineer!
Where are your courses? Thank you.
Hi Paula,
Hope you’re fine. If you can see the “Checkout Now” button at the end of the blog. Click that and you’ll land on our Course page where you can find course details.
If you still have any queries, Click to chat over WhatsApp -> https://api.whatsapp.com/send?phone=919328409085
This is my LinkedIn: http://linkedin.com/in/shivam-modi-aa15261a9
Glad to connect.
Thank you and Regards,
Shivam Modi – CEO, Learn Everything AI