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MISY 262-Quiz 5 Questions with Accurate Answers
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Exam of 3 pages for the course MISY at MISY (MISY 262-Quiz 5)
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MISY 262-Quiz 5Confusion Matrix - answer
Accuracy – answer TP+TN/TP+FN+FP+TN
Calculating the percentage of correct predictions
Precision – answer TP/TP+FP
Measures the exactness of the model
Recall – answer TP/TP+FN
Measures the completeness of the model
Confusion Matrix-Definition – answer performance measurement for machine learning
classification problems where output can be two or more classes
*two by two table that contained FOUR outcomes
Logistic Regression - answer Statistical analysis method to predict a binary outcome,
such as yes and no, based on prior observations of a dataset
-2 possible outcomes, yes/no, true/false
Steps to follow for building a Logistic Regression Model - answer1. Importing the
dataset
2. Using the CSV file
3. Importing the libraries
4. Splitting the dataset into Train & Test
5. Apply the logistic regression model
6. Validate the model
WE NEED TO SPLIT THE DATA INTO TWO DATASETS - answersplit<-
sample.split(Loan_ds$approved.splitRatio= 0.7)
TrainData<-subset(Loan_ds, split== "TRUE")
TestData<-subset(Loan_ds, split=="FALSE")
Generate logistic regression model is glm() function - answerlogmodel1<-
glm(approved~credit.score, data= TrainData, family="binomial")
summary(logellmodel1) - printing the model
**Binomial" to tell R is a logistic regression model**
Need to get prediction results
"Response" telling r to return the probability results - answerprob_train<-
predict(logmodel1,TrainData, type="response")
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