Building Logistic Regression Models using XLMiner

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來自 University of Colorado Boulder 的課程

Advanced Business Analytics Capstone

11 個評分

University of Colorado Boulder

Advanced Business Analytics Capstone

11 個評分

課程 5（共 5 門，Specialization Advanced Business Analytics）

The analytics process is a collection of interrelated activities that lead to better decisions and to a higher business performance. The capstone of this specialization is designed with the goal of allowing you to experience this process. The capstone project will take you from data to analysis and models, and ultimately to presentation of insights. In this capstone project, you will analyze the data on financial loans to help with the investment decisions of an investment company. You will go through all typical steps of a data analytics project, including data understanding and cleanup, data analysis, and presentation of analytical results. For the first week, the goal is to understand the data and prepare the data for analysis. As we discussed in this specialization, data preprocessing and cleanup is often the first step in data analytics projects. Needless to say, this step is crucial for the success of this project. In the second week, you will perform some predictive analytics tasks, including classifying loans and predicting losses from defaulted loans. You will try a variety of tools and techniques this week, as the predictive accuracy of different tools can vary quite a bit. It is rarely the case that the default model produced by ASP is the best model possible. Therefore, it is important for you to tune the different models in order to improve the performance. Beginning in the third week, we turn our attention to prescriptive analytics, where you will provide some concrete suggestions on how to allocate investment funds using analytics tools, including clustering and simulation based optimization. You will see that allocating funds wisely is crucial for the financial return of the investment portfolio. In the last week, you are expected to present your analytics results to your clients. Since you will obtain many results in your project, it is important for you to judiciously choose what to include in your presentation. You are also expected to follow the principles we covered in the courses in preparing your presentation.

從本節課中

Module 2 - Perform predictive analytics tasks

This week you will perform some predictive analytics tasks, including classifying loans and predicting losses from defaulted loans. You will try a variety of tools and techniques this week, as the predictive accuracy of different tools can vary quite a bit. It is rarely the case that the default model produced by ASP is the best model possible. Therefore, it is important for you to tune the different models in order to improve the performance.This week’s assignments require you to build predictive models for both classification and regression tasks. <p> Before working on the assignments, you may review a few videos to remind yourself several important concepts, such as cross validation. These concepts are discussed in the videos Cross Validation and Confusion Matrix and Assessing Predictive Accuracy Using Cross-Validation. You may also find a refresher on XLMiner useful. The videos Building Logistic Regression Models using XLMiner and How to Build a Model using XLMiner discuss how to build logistic regression and linear regression models. Depending on your needs, you may also go back to the videos that discuss how to build trees and neural networks. </p>

Cross Validation and Confusion Matrix5:03

Assessing Predictive Accuracy Using Cross-Validation5:18

Building Logistic Regression Models using XLMiner6:45

How to Build a Model using XLMiner8:02

與講師見面

Manuel Laguna
Professor
Leeds School of Business
Dan Zhang
Professor
Leeds School of Business
David Torgerson
Instructor

Welcome.

In this video you will learn how to use XLMiner to perform logistical regression.

Throughout the video, I will use a medical appointment dataset.

I will first demonstrate how to build logistical regression model

with one predictor variable,

and then proceed to show you how to build a multiple logistic regression model

with multiple predictor variables.

At the end, I will show how to use XLMiner to partition the dataset

and perform cross-validation.

Here is the medical appointment data we discussed before.

To perform a logistic regression with one predictor variable,

we're going to use two columns: Status is used as the target variable

and the Lag is used as the predictor variable.

If you have XLMiner properly installed,

you should see the XLMiner ribbon when you bring up Excel.

To perform logistic regression, click on Classify and Logistic Regression.

Note that all variables are listed here.

Choose Lag and move it to Selected Variables and set Status as Output Variable.

At the bottom of the window, we need to specify Success class.

Since you are interested in predicting appointment cancellation,

you should check the box and select Cancelled in the drop-down menu.

Also note that the default value for cut-off probability is 0.5. Click Next.

You will see a number of options.

We skip them for now and click Finish.

This creates three new output sheets in Excel workbook.

Let's take a closer look at output sheet named LR Output.

At the top of the window is the Output Navigator,

which can lead you to different sections of the output.

Let's go down to the Regression Model section.

As we can see here, the coefficient estimates are -1.7431 and 0.01658.

This table also gives us the p-values which are close to zero for both coefficients,

indicating that the model is statistically significant.

There are some additional summary statistics in the table on the right.

In particular is the multiple R-squared is 0.03179.

Note that the multiple R-squared is a pseudo R-squared value

and does not share the same interpretation as R-squared from the linear regression model.

It also reports a residual deviance of 7,663.

Both the pseudo R-squared

and the residual deviance can be used to compare different models.

Larger values of pseudo R-squared and smaller values of residual deviance are preferred.

In the lower portion of the worksheet,

a summary report on the predictive performance is given.

Since we did not partition the data,

the result is based on applying the models of the whole data-set,

which is used as the training data.

Building the multiple logistic regression model follows almost the exact same steps.

Let's first return to worksheet with the data.

We would like to add the Gender variable to the model.

Before building the model, we first create a dummy variable for Gender.

Click Transform Categorical Data, Create Dummies.

In the pop-out window, move Gender to Variables to be Factored, and click OK.

This creates a new sheet called CreateDummies.

Note that in the Data section, two additional columns are added.

The second to last column is Gender_F,

where the value is one if the Gender is F and zero, otherwise.

The last column is called Gender_M,

where the value is one if Gender equal to M and zero, otherwise.

Click Classify and Logistic Regression.

Move Lag and Gender_M to Selected Variables and set Status as Output Variable.

Note that about choosing the variable Gender_M,

we choose to set the male to one and the female to zero.

Clicking Finish creates three additional sheets.

Go to the Regression Model section in the output.

We see that now we have three coefficient estimates.

The coefficient for Gender_M variable is -0.3572.

Since Gender_M equal one for male and zero for female, this suggests that the log-odds,

and consequently the probability of a cancellation, is smaller for male patients.

Also, the multiple R-squared value is 0.03586 and the residual deviance is 7,631.

Hence, by including the Gender variable, we increase the multiple R-squared value

and decrease the residual deviance.

Our last topic in this video is cross-validation.

The first step in cross-validation is to divide the data into training

and the validation sets.

Go back to the CreateDummies sheet,

which contains the updated data set with dummy variables.

Click Partition and then Standard Partition.

In the pop-out window, we can choose which variable to include.

We choose to include all variables by moving them to Selected Variables.

On the bottom, we can set the percentage for the training and validation set.

We accept the default 60-40 split.

Note that you can also choose to set your own percentage,

in which case you can divide the data into three sets instead of two sets,

where the last set is called the test set.

In order to replicate the results you see on the screen,

make sure that you check the Set seed checkbox and put 12345 in the input box.

Since the partition is randomly selected,

by setting the seed of the random number generator, you ensure

that the partition you have is exactly the same as mine.

Otherwise, you result will differ slightly from mine

because you are using a different data split for training and the validation.

Clicking OK bring us to a new worksheet with the partition data.

As you can see, there are 4,478 rows in the training data

and 2,985 rows in the validation set.

Now, we can bring out the logistic regression window, as before.

Observe that the worksheet is filled with Data_Partition,

which is a worksheet we just created.

Again, we're building logistic regression model with Status as Output Variable

and Lag and Gender_M as predictor variables.

Compared to the case without cross-validation,

now the output contains a summary report for Validation Data Scoring

which is different from the Training Data Scoring results.

The coefficient estimates are also a little different than before.

This is because when we perform cross-validation,

only the training data is used to build the model.

Note that in the confusion matrix, the class Cancelled is listed first.

This is slightly different from the ones used in other lectures.

The next worksheet was LR_ValidationLiftChart [inaudible] contains both the lift chart

and ROC curve.

As we discussed before, they can be used to compare different models.

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