Here are this week's Data Science Interview Questions along with the correct answer
Thread π§΅π
#MachineLearning #Python #100DaysOfCode
Thread π§΅π
#MachineLearning #Python #100DaysOfCode
Answer by @josh_ko_naman
1) SL has a feedback mechanism.
UL has no feedback mechanism.
2) Supervised learning involves building a model for predicting, or estimating.
In unsupervised learning, we can learn relationships and structures from data
1) SL has a feedback mechanism.
UL has no feedback mechanism.
2) Supervised learning involves building a model for predicting, or estimating.
In unsupervised learning, we can learn relationships and structures from data
https://twitter.com/PiyalBanik/status/1412400894105198592?s=20
Answer by @ammaryh92 & @arunkumarai
-regularization
-simpler model architecture
-more training data
-reduce noise in the data
-reduce the number of input attributes
-shorter training cycles
-regularization
-simpler model architecture
-more training data
-reduce noise in the data
-reduce the number of input attributes
-shorter training cycles
https://twitter.com/PiyalBanik/status/1412742903965708290?s=20
Partial answer by @SpeculatorDon
Filter Methods
- Linear discrimination analysis
- ANOVA
- Chi-Square
Wrapper Methods
- Forward Selection
- Backward Selection
- Recursive Feature Elimination
Filter Methods
- Linear discrimination analysis
- ANOVA
- Chi-Square
Wrapper Methods
- Forward Selection
- Backward Selection
- Recursive Feature Elimination
https://twitter.com/PiyalBanik/status/1413113134219370498?s=20
Answer by @Shivu2021 & @josh_ko_naman
We use the elbow method to select k for k-means clustering.
At the point at which the graph gets smoother than that value, we select.
We use the elbow method to select k for k-means clustering.
At the point at which the graph gets smoother than that value, we select.
https://twitter.com/PiyalBanik/status/1413510583169650689?s=20
Answer by @Shivu2021
The recommendation engine is accomplished with collaborative filtering. Collaborative filtering explains the behavior of other users and their purchase history in terms of ratings, selection, etc.
The recommendation engine is accomplished with collaborative filtering. Collaborative filtering explains the behavior of other users and their purchase history in terms of ratings, selection, etc.
https://twitter.com/PiyalBanik/status/1413844298244661255?s=20
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