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Predictive Modeling with Logistic Regression using SAS

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Predictive Modeling with Logistic Regression using SAS

Dieser Kurs ist Teil von SAS Statistical Business Analyst (berufsbezogenes Zertifikat)

Unterrichtet in Deutsch (KI-Synchronisation)

Dozent: Marc Huber

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6 Module

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6 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

63 Bewertungen

Stufe Mittel

Einige einschlägige Kenntnisse erforderlich

2 Wochen zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Kompetenzen, die Sie erwerben

Kategorie: Correlation Analysis
Kategorie: Logistic Regression
Kategorie: Regression Analysis
Kategorie: Performance Analysis
Kategorie: Model Evaluation
Kategorie: Feature Engineering
Kategorie: Statistical Analysis
Kategorie: Data Preprocessing
Kategorie: Big Data
Kategorie: Predictive Analytics
Kategorie: Data Management
Kategorie: Predictive Modeling
Kategorie: Statistical Modeling

Werkzeuge, die Sie lernen werden

Kategorie: Statistical Software
Kategorie: SAS (Software)

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40 Aufgaben

Unterrichtet in Deutsch (KI-Synchronisation)

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In diesem Kurs gibt es 6 Module

This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors. You also learn to assess model performance and compare models.

Moduldetails

Welcome! In this module, you will review the fundamentals of predictive modeling. First we'll get you started by setting up the course environment. Then you explore the business scenario data that is used throughout the course. You’ll learn the goals of predictive modeling, key terms and model elements, and the basic workflow used to build predictive models, along with common real-world applications. You’ll also work through practical scenarios to explore data using descriptive statistics and frequency tables, and you’ll examine the code used to generate these summaries. Finally, you’ll learn about common data and analytical challenges.

Das ist alles enthalten

16 Videos6 Lektüren6 Aufgaben

16 VideosInsgesamt 34 Minuten

Meet the Instructor1 Minute
Overview1 Minute
Introduction0 Minuten
Goals of Predictive Modeling2 Minuten
Terms for Elements in Predictive Modeling1 Minute
Basic Steps of Predictive Modeling3 Minuten
Applications of Predictive Modeling2 Minuten
Demonstration Scenario: Target Marketing for a Bank2 Minuten
Demo: Examining the Code for Generating Descriptive Statistics and Frequency Tables2 Minuten
Introduction0 Minuten
Data Challenges6 Minuten
Analytical Challenges2 Minuten
Separate Sampling2 Minuten
Avoiding the Optimism Bias: Honest Assessment2 Minuten
Splitting the Data for Model Training and Assessment3 Minuten
Demo: Splitting the Data5 Minuten

6 LektürenInsgesamt 46 Minuten

What You Learn in This Course5 Minuten
Learner Prerequisites1 Minute
Access SAS Software & Set up Data10 Minuten
About the Demos and Practices in this Course10 Minuten
Frequently Asked Questions10 Minuten
Summary10 Minuten

6 AufgabenInsgesamt 100 Minuten

Practice: Exploring the Bank Data for the Target Marketing Project20 Minuten
Practice: Exploring the Veterans' Organization Data Used in the Practices20 Minuten
Knowledge Check: Model Training & Sampling5 Minuten
Knowledge Check: Splitting the Data5 Minuten
Practice: Splitting the Data20 Minuten
Module-End Graded Assessment30 Minuten

In this module, you investigate the concepts behind the logistic regression model. Then you learn to use the LOGISTIC procedure to fit a logistic regression model. Finally, you learn how to score new cases and adjust the model for oversampling.

Das ist alles enthalten

18 Videos1 Lektüre4 Aufgaben

18 VideosInsgesamt 54 Minuten

Overview1 Minute
Introduction0 Minuten
Understanding the Logistic Regression Model3 Minuten
Constraining the Posterior Probability Using the Logit Transformation2 Minuten
Understanding the Fitted Surface1 Minute
Interpreting the Model by Calculating the Odds Ratio3 Minuten
Understanding Logistic Discrimination2 Minuten
Estimating Unknown Parameters Using Maximum Likelihood Estimation2 Minuten
Interpreting Concordant, Discordant, and Tied Pairs2 Minuten
Using PROC LOGISTIC to Fit Logistic Regression Models0 Minuten
Demo: Fitting a Basic Logistic Regression Model, Part 18 Minuten
Demo: Fitting a Basic Logistic Regression Model, Part 212 Minuten
Scoring New Cases0 Minuten
Demo: Scoring New Cases8 Minuten
Introduction0 Minuten
Understanding the Effect of Oversampling1 Minute
Understanding the Offset2 Minuten
Demo: Correcting for Oversampling7 Minuten

1 LektüreInsgesamt 10 Minuten

Summary10 Minuten

4 AufgabenInsgesamt 60 Minuten

Question 2.015 Minuten
Question 2.025 Minuten
Practice: Fitting a Logistic Regression Model20 Minuten
Fitting the Model Review30 Minuten

In this module, you learn how to deal with common problems with your predictor variables such as missing values, categorical predictors with many levels, a high number of redundant predictors, and nonlinear relationships with the response variable.

Das ist alles enthalten

26 Videos9 Aufgaben

26 VideosInsgesamt 76 Minuten

Overview1 Minute
Introduction0 Minuten
Reasons for Missing Data3 Minuten
Complete Case Analysis2 Minuten
Methods for Imputing Missing Values3 Minuten
Missing Value Imputation with Missing Value Indicator Variables4 Minuten
Demo: Imputing Missing Values4 Minuten
Cluster Imputation2 Minuten
Introduction0 Minuten
Problems Caused by Categorical Inputs4 Minuten
Solutions to Problems Caused by Categorical Inputs1 Minute
Linking to Other Data Sets1 Minute
Collapsing Categories by Thresholding1 Minute
Collapsing Categories by Using Greenacre's Method3 Minuten
Demo: Collapsing the Levels of a Nominal Input, Part 16 Minuten
Demo: Collapsing the Levels of a Nominal Input, Part 210 Minuten
Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding3 Minuten
Demo: Computing the Smoothed Weight of Evidence5 Minuten
Introduction0 Minuten
Problem of Redundancy2 Minuten
Variable Clustering Method1 Minute
Understanding Principal Components5 Minuten
Divisive Clustering4 Minuten
PROC VARCLUS Syntax1 Minute
Selecting a Representative Variable from Each Cluster1 Minute
Demo: Reducing Redundancy by Clustering Variables9 Minuten

9 AufgabenInsgesamt 105 Minuten

Question 3.015 Minuten
Practice: Imputing Missing Values20 Minuten
Question 3.025 Minuten
Question 3.035 Minuten
Question 3.045 Minuten
Practice: Collapsing the Levels of a Nominal Input20 Minuten
Practice: Computing the Smoothed Weight of Evidence20 Minuten
Question 3.055 Minuten
Practice: Reducing Redundancy by Clustering Variables20 Minuten

In this module, you learn how to select the most predictive variables to use in your model.

Das ist alles enthalten

23 Videos1 Lektüre12 Aufgaben

23 VideosInsgesamt 92 Minuten

Introduction0 Minuten
Detecting Nonlinear Relationships4 Minuten
Demo: Performing Variable Screening, Part 16 Minuten
Demo: Performing Variable Screening, Part 24 Minuten
Univariate Binning and Smoothing3 Minuten
Demo: Creating Empirical Logit Plots10 Minuten
Remedies for Nonlinear Relationships2 Minuten
Demo: Accommodating a Nonlinear Relationship, Part 16 Minuten
Demo: Accommodating a Nonlinear Relationship, Part 28 Minuten
Introduction0 Minuten
Specifying a Subset Selection Method in PROC LOGISTIC2 Minuten
Best-Subsets Selection1 Minute
Stepwise Selection3 Minuten
Backward Elimination2 Minuten
Scalability of the Subset Selection Methods in PROC LOGISTIC3 Minuten
Detecting Interactions3 Minuten
BIC-based Significance Level3 Minuten
Demo: Detecting Interactions7 Minuten
Demo: Using Backward Elimination to Subset the Variables4 Minuten
Demo: Displaying Odds Ratios for Variables Involved in Interactions4 Minuten
Demo: Creating an Interaction Plot3 Minuten
Demo: Using the Best-Subsets Selection Method4 Minuten
Demo: Using Fit Statistics to Select a Model10 Minuten

1 LektüreInsgesamt 10 Minuten

Summary of Preparing the Input Variables, Parts 1 and 210 Minuten

12 AufgabenInsgesamt 160 Minuten

Question 3.065 Minuten
Practice: Performing Variable Screening20 Minuten
Practice: Creating Empirical Logit Plots20 Minuten
Question 3.075 Minuten
Question 3.085 Minuten
Question 3.095 Minuten
Practice: Using Forward Selection to Detect Interactions20 Minuten
Question 3.105 Minuten
Practice: Using Backward Elimination to Subset the Variables20 Minuten
Question 3.115 Minuten
Practice: Using Fit Statistics to Select a Model20 Minuten
Preparing the Input Variables Review30 Minuten

In this module, you learn how to assess the performance of your model and how to determine allocation rules that maximize profit. Finally, you learn how to generate a family of increasingly complex predictive models and how to select the best model.

Das ist alles enthalten

30 Videos1 Lektüre9 Aufgaben

30 VideosInsgesamt 78 Minuten

Overview1 Minute
Introduction0 Minuten
Fit versus Complexity2 Minuten
Assessing Models when Target Event Data Is Rare2 Minuten
Demo: Preparing the Validation Data5 Minuten
Introduction0 Minuten
Understanding the Confusion Matrix5 Minuten
Measuring Performance across Cutoffs by Using the ROC Curve4 Minuten
Choosing Depth by Using the Gains Chart3 Minuten
Effects of Oversampled Data on Performance Measures3 Minuten
Adjusting a Confusion Matrix for Oversampling1 Minute
Demo: Measuring Model Performance Based on Commonly-Used Metrics7 Minuten
Introduction0 Minuten
Understanding the Effect of Cutoffs on Confusion Matrices1 Minute
Understanding the Profit Matrix2 Minuten
Choosing the Optimal Cutoff by Using the Profit Matrix3 Minuten
Using the Central Cutoff1 Minute
Using Profit to Assess Fit0 Minuten
Calculating Sampling Weights1 Minute
Demo: Using a Profit Matrix to Measure Model Performance6 Minuten
Introduction0 Minuten
Plotting Class Separation2 Minuten
Assessing Overall Predictive Power3 Minuten
Demo: Using the K-S Statistic to Measure Model Performance2 Minuten
Introduction0 Minuten
Comparing ROC Curves of Several Models"2 Minuten
Demo: Comparing ROC Curves to Measure Model Performance4 Minuten
Using Macros to Compare Many Models1 Minute
Demo: Comparing and Evaluating Many Models, Part 18 Minuten
Demo: Comparing and Evaluating Many Models, Part 27 Minuten

1 LektüreInsgesamt 10 Minuten

Summary10 Minuten

9 AufgabenInsgesamt 85 Minuten

Question 4.015 Minuten
Question 4.025 Minuten
Question 4.035 Minuten
Practice: Assessing Model Performance20 Minuten
Question 4.045 Minuten
Question 4.055 Minuten
Question 4.065 Minuten
Question 4.075 Minuten
Measuring Model Performance Review30 Minuten

Das ist alles enthalten

1 Lektüre1 App-Element

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Dozent

Lehrkraftbewertungen

(18 Bewertungen)

Marc Huber

SAS

2 Kurse8.853 Lernende

von

SAS

Bewertungen von Lernenden

5 stars
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4 stars
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3 stars
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2 stars
3,17 %
1 star
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Zeigt 3 von 63 an

Geprüft am 10. Apr. 2021

Great training sets of problems. Good guidance & teaching.

Geprüft am 14. Juni 2021

Thank you so much to the instructor, Michael J Patetta for teaching this course!

Geprüft am 30. Dez. 2022

Very completed and deep knowledge shared with very friendly ways, explained the knowledge very clearly. Also the practices help me to understand the knowledge better.

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