Classification 1 Quiz
This quiz is part of Algoritma Academy assessment process. Congratulations on completing the first Classification in Machine Learning course! We will conduct an assessment quiz to test practical classification model techniques you have learned on the course. The quiz is expected to be taken in the classroom, please contact our team of instructors if you missed the chance to take it in class.
To complete this assignment, you are required to build your classification model to classify the characteristics of employees who have resigned and have not. Use Logistic Regression and k-Nearest Neighbor algorithms by following these steps:
Data Exploration
Let us start by preparing and exploring the data first. In this quiz, you will be using the turnover of employee data (turnover). The data is stored as a .csv format in this repository as turnover_balance.csv file. Import your data using read.csv or read_csv and save as turnover object. Before building your classification model, you will need to perform an exploratory analysis to understand the data. Glimpse the structure of our turnover data! You can choose either str() or glimpse() function.
# your code here
Turnover data consists of 10 variables and 7.142 rows. This dataset is a human resource data that shows historical data of employee characteristics who will resign or not. Below is more information about the variable in the dataset:
satisfaction_level: the level of employee satisfaction working in a companylast_evaluation: employee satisfaction level at the last evaluationnumber_project: the number of projects the employee has receivedaverage_monthly_hours: average hours worked per monthtime_spend_company: length of time in the company (years)work_accident: presence or absence of work accident, 0 = none, 1 = therepromotion_last_5years: ever got a promotion in the last 5 years, 0 = no, 1 = yesdivision: name of department or divisionsalary: income level, divided into low, medium and highleft: employee history data resigned, 0 = no, 1 = yes
In this quiz, we will try to predict whether or not the employee has a resignation tendency using the left column as our target variable. Please change the class of Work_accident, left, and promotion_last_5years column to be in factor class as it should be.
# your code here
For example, as an HR staff, we are instructed to investigate divisions that has a long history of an employee resigning and analyze their average monthly hours. Let's do some aggregation of average_monthly_hours for each division. Because you only focused on the employee who left, you should filter the historical data with the condition needed.
Using dplyr functions, you can use filter(), then group_by() function by division and summarise() the mean of average_monthly_hours, then arrange it based on average_monthly_hours from high to low using arrange() function.
As an alternative, if you are more familiar using base R code style, you can filter the data using conditional sub setting data["condition needed",], than assign it into df_left object. After that, you can aggregate df_left based on division and average_monthly_hours column using aggregate() function. Don't forget to use mean in FUN parameter and assign it into df_agg. In order to get the ordered mean value from high to low of the average_monthly_hours, you can use order() function in conditional sub setting data[order(column_name, decreasing = T), ].
# your code here
- Based on the aggregation data that you have analyzed, which division has the highest average of monthly hours?
- [ ] Marketing division
- [ ] Technical division
- [ ] Sales division
- [ ] Accounting division
Data Preprocessing
After conducting the data exploratory, we will go ahead and perform preprocessing steps before building the classification model. Before we build the model, let us take a look at the proportion of our target variable in the left column using prop.table(table(data)) function.
# your code here
It seems like our target variable has a balance proportion between both classes. Before we build the model, we should split the dataset into train and test data in order to perform model validation. Split turnover dataset into 80% train and 20% test proportion using sample() function and use set.seed() with the seed 100. Store it as a train and test object.
Notes: Make sure you use RNGkind() before splitting
RNGkind(sample.kind = "Rounding")
set.seed(100)
# your code here
Let's take a look distribution of proportion in train and test data using prop.table(table(data)) function to make sure in train and test data has balance or not distribution of each class target. Please round the proportion using two decimal numbers using the round() function.
# your code here
- Based on the proportions of
train and test, can the distribution of each class be considered balanced? Why do we need to ensure that each class has a balanced proportion especially in the training data set?
- [ ] No, it is not.
- [ ] Yes, it is, but it is not necessary to balance the class proportion.
- [ ] No, it is not. The distribution of each class needs to be balanced to prevent any misclassified observation.
- [ ] Yes, it is. The distribution of each class in training set data needs to be balanced so when doing model fitting, the algorithm can learn the characteristics for each class equally.
Logistic Regression Model Fitting
After we have split our dataset in train and test set, let's try to model our left variable using all of the predictor variables to build a logistic regression. Please use the glm(formula, data, family = "binomial") to do that and store your model under the model_logistic object. Remember, we are not using turnover dataset any longer, and we will be using train dataset instead.
# model_logistic <- glm()
Based on the model_logictic you have made above, take a look at the summary of your model using summary() function.
# your code here
- Logistic regression is one of interpretable model. We can explain how likely each variable are predicted to the class we observed. Based on the model summary above, what can be interpreted from the
Work_accident1 coeficient?
- [ ] The probability of an employee that had a work accident not resigning is 0.21.
- [ ] Employee who had a work accident is about 0.21 more likely to resign than the employee who has not.
- [ ] Employee who had a work accident is about 1.57 less likely to resign than the employee who has not.
K-Nearest Neighbor Model Fitting
Now let's try to explore the classification model using the k-Nearest Neighbor algorithm. In the k-Nearest Neighbor algorithm, we need to perform one more step of data preprocessing. For both our train and test set, drop the categorical variable from each column except our left variable. Separate the predictor and target in-out train and test set.
# predictor variables in `train`
train_x <-
# predictor variables in `test`
test_x <-
# target variable in `train`
train_y <-
# target variable in `test`
test_y <-
Recall that the distance calculation for kNN is heavily dependent upon the measurement scale of the input features. If any variable that have high different range of value could potentially cause problems for our classifier, so let's apply normalization to rescale the features to a standard range of values.
To normalize the features in train_x, please using scale() function. Meanwhile, in testing set data, please normalize each features using the attribute center and scale of train_x set data.
Please look up to the following code as an example to normalize test_x data:
scale(data_test, center = attr(data_train, "scaled:center"),
scale = attr(data_train, "scaled: scale"))
Now it's your turn to try it in the code below:
# your code here
# scale train_x data
train_x <- scale()
# scale test_x data
test_x <- scale()
After we have done performing data normalizing, we need to find the right K to use for our K-NN model. In practice, choosing k depends on the difficulty of the concept to be learned and the
number of records in the training set data.
- The method for getting K value, does not guarantee you to get the best result. But, there is one common practice for determining the number of K. What method can we use to choose the number of k?
- [ ] square root by number of row
- [ ] number of row
- [ ] use k = 1
After answering the questions above, please find the number of k in the following code:
Hint: If you have got a decimal number, do not forget to round it and make sure you end up with an odd number to prevent voting tie break.
# your code here
Using K value, we have calculated in the section before, try to predict test_y using train_x dan train_y dataset. To make the k-nn model, please use the knn() function and store the model under the model_knn object.
Next, please look up at the following code:
library(class)
model_knn <- knn(train = ______, test = ________, cl = _______, k = _____)
- Fill the missing code here based on the picture above and choose the right code for building the knn model!
- [ ] model_knn <- knn(train = train_y, test = test_y, cl = test_y, k = 75)
- [ ] model_knn <- knn(train = train_x, test = test_y, cl = test_x, k = 89)
- [ ] model_knn <- knn(train = train_x, test = test_x, cl = train_y, k = 75)
- [ ] model_knn <- knn(train = train_x, test = train_y, cl = train_x, k = 89)
Prediction
Now let's get back to our model_logistic. In this section, try to predict test data using model_logistic return the probability value using predict() function with type = "response" in the parameter function and store it under prob_value object.
prob_value <-
Because the prediction results in the logistic model are probabilities, we have to change them to categorical / class according to the target class we have. Now, given a threshold of 0.45, try to classify whether or not an employee can be predicted to resign. Please use ifelse() function and store the prediction result under the pred_value object.
pred_value <-
Based on the prediction value above, try to answer the following question.
- In the prescriptive analytics stage, the prediction results from the model will be considered for business decision making. So, please take your time to check the prediction results. How many predictions do our
model_logistic generate for each class?
- [ ] class 0 = 714, class 1 = 715
- [ ] class 0 = 524, class 1 = 905
- [ ] class 0 = 590, class 1 = 839
Model Evaluation
In the previous sections, we have performed a prediction using both Logistic Regression and K-NN algorithm. However, we need to validate whether or not our model did an excellent job of predicting unseen data. In this step, try to make the confusion matrix of model performance in the logistic regression model based on test data and pred_value and use the positive class is "1".
Note: do not forget to do the explicit coercion as.factor().
# your code here
Make the same confusion matrix for model_knn prediction result of test_y.
# your code here
Let's say that we are working as an HR staff in a company and are utilizing this model to predict the probability of an employee resigning. As an HR, we would want to know which employee has a high potential of resigning so that we can take a precautionary approach as soon as possible. Now try to answer the following questions.
- Which one is the right metric for us to evaluate the numbers of resigning employees that we can detect?
- [ ] Recall
- [ ] Specificity
- [ ] Accuracy
- [ ] Precision
- Using the metrics of your answer in the previous question, which of the two models has a better performance in detecting resigning employees?
- [ ] Logistic Regression
- [ ] K-Nearest Neighbor
- [ ] Both has more or less similar performance
- Now, recall what we have learned the advantage of each model. Which one is more suitable to use if we aimed for model interpretability?
- [ ] K-NN, because it tends to have a higher performance than logistic regression
- [ ] Logistic regression, because it has a lower performance than K-nn
- [ ] Logistic regression, because each coefficient can be transformed into an odds ratio
- [ ] K-NN, because it results in a better precision score for the positive class