Supervised Machine Learning Algorithms
1. Penalized Regression Penalized regression is a technique that is useful for reducing/shrinking a large number of features to a manageable set and for making good predictions in a variety of large data sets. It is used to avoid overfitting….
Overfitting and Methods of Addressing it
Overfitting is a problem that arises when the machine learning algorithm fits the training data too well, making it unable to predict well using new data. Overfitting means training a model to such a degree of specificity to the training…
Supervised Machine Learning, Unsupervised Machine Learning, and Deep Learning
Machine learning employs the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. It has an iterative aspect in that when models are exposed to new data, they can adapt independently. Machine learning…
Study Notes for CFA® Level II – Quantitative Methods – offered by AnalystPrep
Reading 0: Introduction to Linear Regression (Now Part of Level I) -a. Describe a simple linear regression model and the roles of the dependent and independent variables in the model; -b. Describe the least squares criterion, how it is used…
Multiple Regression Model
Consider the multiple regression of the price of the US Dollar index on inflation rates and real interest rates. The estimated regression model is expressed as: $$P=81-276INF+902IR$$ Where: P = Price of USDX. INF = Inflation rate. IR = Real…
Estimated Logistic Regression
Qualitative (categorical) dependent variables are the dummy variables used as dependent variables rather than independent variables. Recall that a dummy variable is a variable that takes on the value 0 or 1.The logistic transformation is the most widely used. It…
Model Misspecification
Model specification involves selecting independent variables to include in the regression and the functional form of the regression equation. We say that a model is misspecified when it violates the assumptions underlying linear regression, its functional form is incorrect, or…
Multicollinearity
Multicollinearity occurs when two or more independent variables are significantly correlated to each other. It results from the violation of the multiple regression assumptions that there is no apparent linear relationship between two or more of the independent variables. Multicollinearity…
Heteroskedasticity and Serial Correlation
One of the assumptions underpinning multiple regression is that regression errors are homoscedastic. In other words, the variance of the error terms is equal for all observations: $$E(\epsilon_{i}^{2})=\sigma_{\epsilon}^{2}, i=1,2,…,n$$ In reality, the variance of errors differs across observations. This is…
Hypothesis Testing
Hypothesis testing involves testing an assumption regarding a population parameter. A null hypothesis is a condition believed not to be true. We reject the null hypothesis in the presence of enough evidence against it and accept the alternative hypothesis. Hypothesis…
