Predictive Modeling with Logistic Regression using SAS

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Predictive Modeling with Logistic Regression using SAS

Ce cours fait partie de Certificat Professionnel SAS Statistical Business Analyst

Enseigné en Français (doublage IA)

Instructeur : Marc Huber

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6 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

63 avis

niveau Intermédiaire

Certaines connaissances prérequises

2 semaines à compléter

à 10 heures par semaine

6 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

63 avis

niveau Intermédiaire

Certaines connaissances prérequises

2 semaines à compléter

à 10 heures par semaine

Compétences que vous acquerrez

Catégorie : Model Training
Catégorie : Feature Engineering
Catégorie : Sampling (Statistics)
Catégorie : Logistic Regression
Catégorie : Supervised Learning
Catégorie : Regression Analysis
Catégorie : Model Evaluation
Catégorie : Predictive Modeling
Catégorie : Statistical Analysis
Catégorie : Predictive Analytics
Catégorie : Business Analytics
Catégorie : Statistical Methods
Catégorie : Statistical Modeling
Catégorie : Data Preprocessing

Outils que vous découvrirez

Catégorie : SAS (Software)
Catégorie : Statistical Software

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Enseigné en Français (doublage IA)

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Il y a 6 modules dans ce cours

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.

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.

Inclus

16 vidéos6 lectures6 devoirs

16 vidéosTotal 34 minutes

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

6 lecturesTotal 46 minutes

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

6 devoirsTotal 100 minutes

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

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.

Inclus

18 vidéos1 lecture4 devoirs

18 vidéosTotal 54 minutes

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

1 lectureTotal 10 minutes

Summary10 minutes

4 devoirsTotal 60 minutes

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

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.

Inclus

26 vidéos9 devoirs

26 vidéosTotal 76 minutes

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

9 devoirsTotal 105 minutes

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

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

Inclus

23 vidéos1 lecture12 devoirs

23 vidéosTotal 92 minutes

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

1 lectureTotal 10 minutes

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

12 devoirsTotal 160 minutes

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

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.

Inclus

30 vidéos1 lecture9 devoirs

30 vidéosTotal 78 minutes

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

1 lectureTotal 10 minutes

Summary10 minutes

9 devoirsTotal 85 minutes

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

Inclus

1 lecture1 élément d'application

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Instructeur

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Marc Huber

SAS

2 Cours8 936 apprenants

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SAS

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