INFOMDA1 Review Notes
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General
README
👉0 Content
0. SLV General
👉1 Resources
1 Resources - 考前必打开网页
1.1 SLV Course Homepage - Example Codes
1.2 ChatGPT - Answers
1.3 Example Exam - Question Types, For Review
1.4 Cheatsheets
👉2 Exam Qs
2. Exam Questions
2.1 Theoretical - Data Visualization
2.2 Theoretical - Improving Plot, Explaining
2.3 Theoretical - Energy Prediction
2.4 Practical - Decision Rule
2.5 Practical - LASSO
👉3. Topics & Codes
3. Content
3.1 Data Wrangling - 数据类型，新建，摘要
3.2 Graphics - 绘图
3.3 EDA - 查看、探索数据
3.4 Linear Regression - CI, MSE, Split, Cross Validation
3.5 Classification - KNN, Confusion Matrix, LR, LDA (Used For Predicting Categorical Variables)
3.6 Classification Evaluation - Confusion Matrix, LR, LDA, Classification Trees, Random Forest
3.7 Nonlinear Regression - Prediction Plot, Poly
3.8 Ensemble Methods - Bagging, RF, Boosting, Xgboost
On this page
1.3 Example Exam - Question Types, For Review
https://dgoretzko.github.io/slv/practice_exam/SLV_Practice.pdf