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Interview of Data Science - Coggle Diagram
Interview of Data Science
Models
classification
Naive Bayes
Ensemble
Gradient boosting
XGBoosting
Bagging
Random forest
Stacking
Regression
Logistic regression
sigmoid-function
multiple regression
multicollinearity
Linear regression
Assumptions required for linear regression
Decision Tree
Overfitting
Unsupervised
SVM
Evaluation of model
R-squared/Adjusted R-squared:
MSE
Confusion matrix
ROC
F1 score
Accuracy and performance
Bias and variance tradeoffs
Cross validation
ANOVA
Feature engineering
Dimension reduction
PCA
Box-Cox transformation
Transform to reduce skew
Scaling
Log transform
Normalization
Standardization
Basic statistics
P value
Power
TypeI and TypeII error
Computation
SQL
Python
Algorithm