# Analytics Topics for First Exam

## Generally

- Do not need to memorize any formulas
- Do not need to be prepared to use Alteryx, DataRobot, or any tool besides pen-and-paper for simple math calculations
- Do need to conceptually understand the principles of business analytics that we have discussed so far
- Do not need to understand principles in the book that we have not discussed in class…
- But the book will be monumentally helpful towards your conceptual understanding of what we have discussed.

## Supplemental Book Chapters

- 1,2,3,4,5,7,11

## Conceptual from first half of class

- CRISP-DM and each of its phases
- Unsupervised vs supervised learning
- The strictly conceptual need for and use of the following for predictive analytics:
- ETL
- Transformations / feature engineering
- Joining, summarising

## Lecture: Introduction to Predictive Analytics

- Slides 2-6
- Conceptual understanding of how linear models are formulaically represented and for how predictions are made based off x-values and the weights the model assigns to each predictor
- Conceptual understanding of the need for dummy-coding for linear models, and for the use of reference levels in models with an intercept

## Lecture: Supervised Segmentation

- Conceptual understanding of the use of supervised segmentation, of information gain, of “informative attributes”, of entropy
- Ability to interpret Entropy vs Proportion plots
- Understanding of how to obtain a probability prediction of class membership from a decision tree
- Understand conceptually the need for and use of Laplace Correction
- Understand the desirability preference between a model providing predictions of discrete values versus predictions of probabilities of class membership.
- Do not need to memorize entropy formulas, information gain formulas, or the formula for laplace correction

## Lecture: Discriminant Functions

- Concept of decision boundaries, and of the ultimate goal being a probability prediction
- Concept of logistic regression, of the need for it to directly predict logOdds, of why linear functions cannot directly predict probability
- Link between probability => odds => logOdds
- Concept of SVM
- Concept of objective functions (i.e., loss functions), which linear regression uses, and which SVM uses
- How to get non-linear models from linear models
- Basic understanding of how each node in a neural network is simply an isolated model, and how outputs of previous models feed into next layer ones.

## Lecture: Model Performance

- Concept of over- and under-fitting
- How to evaluate models:
- Options 1 (in-sample evaluation), 2 (TVH or train-validation), option 3 (x-val) and how each works
- X-val

- Concept of decision tree pruning
- Learning Curves
- Fitting Graphs
- Concept of Regularization for linear models
- Do not need to memorise that ridge involves summing the squares of the weights and that lasso sums the absolute weight values
- Understand conceptually the approach of regularization

- Do not need to understand nested cross-validation
- Be familiar with general partitioning methods (e.g., random, stratified, group)

## Lecture: Decision Analytics Thinking

- Conceptually how accuracy as an evaluation metric can fail
- We didn’t use this precise term in class, but we talked around it. Understand the concept of the “base-rate fallacy.” This is another reason why accuracy is a misleading metric.

- Concept of confusion matrices for binary class prediction evaluation:
- The labeling of individual cells
- The ability to create a basic matrix from simple data
- Do need to memorise how to use a confusion matrix to obtain:
- True Positive Rate (i.e., sensitivity, recall),
- False Positive Rate, and
- Positive Predictive Value (i.e., precision)

- Do not need to memorize the various synonyms for the above three metrics
- Be able to interpret probability-notation-representation of the above metrics:
`p(Y|p) == True Positive Rate`

`p(Y|n) == False Positive Rate`

`p(p|Y) == Positive Predictive Value`

- Understand the two basic uses of the Expected Value Framework, and for how a cost-benefit matrix is obtained