Define precision and recall?
Recall is also known as the true positive rate: the amount of positives your model claims compared to the actual number of positives there are throughout the data.
Precision is also known as the positive predictive value, and it is a measure of the amount of accurate positives your model claims compared to the number of positives it actually claims.
It can be easier to think of recall and precision in the context of a case where you've predicted that there were 10 apples and 5 oranges in a case of 10 apples.
You'd have perfect recall (there are actually 10 apples, and you predicted there would be 10) but 66.7% precision because out of the 15 events you predicted, only 10 (the apples) are correct.
Learn More :
Data Science
- What features would you use to predict the Uber ETA for ride requests?
- How would you evaluate the predictions of an Uber ETA model?
- Describe how you would build a model to predict Uber ETAs after a rider requests a ride.
- Suppose you're working as a data scientist at Facebook. How would you measure the success of private stories on Instagram, where only certain chosen friends can see the story?
- Precision vs Accuracy Vs Recall?
- Error vs variance vs bias?
- False negatives vs false positives? When is either one worse than the other?
- Describe your data science process start to finish?
- Data science vs machine learning vs AI?
- How would you find correlation between a categorical variable and a continuous variable?
- How do you treat null/missing values? Name 3 methodologies.
- How can outlier values be treated?
- What is data normalization? Name 2 normalization methodologies.
- What is the role/importance of data cleaning?
- What are success metrics vs tracking metrics?
- What kind of metric would you make to measure success of a program (marketing) and how do you define them?
- Let's say an app was getting a redesign. How do you know if the redesign was successful?
- We noticed a steep decline in users in a certain area of the world, how would you address/asses?
- What are the two methods used for the calibration in Supervised Learning?
- Which method is frequently used to prevent overfitting?
- What is the difference between heuristic for rule learning and heuristics for decision trees?
- What is Perceptron in Machine Learning?
- Explain the two components of Bayesian logic program?
- What are Bayesian Networks (BN) ?
- Why instance based learning algorithm sometimes referred as Lazy learning algorithm?