SMHS LinearModeling MachineLearning

SMHS Linear Modeling - Machine Learning Algorithms

Scientific inference based on fixed and random effect models, assumptions, and mixed effects logistic regression.

Questions:

• How can we tie human intuition and computer-generated results to obtain reliable, effective, and efficient decision-support system (that facilitates, forecasting)?
• Niels Born – “It is difficult to make predictions, especially about the future” …
• Can we unsupervisely classify the data?

Prediction

For most of the machine learning algorithms (including first-order linear regression), we:

• first generate the model using training data, and then
• predict values for test/new data.

Predictions are made using the R predict function. (type ?predict.name), where name is the function-name corresponding to the algorithm. The first argument of predict often represents the variable storing the model and the second argument is a matrix or data frame of test data that the model needs to be applied to. Calling predict can be done in 2 ways: type predict or type of predict.name.

Example:

#mydata <- read.table('https://umich.instructure.com/files/330381/download?download_frd=1&verifier=HpfmjfMFaMsk7rIpfPx0tmz960oTW7JA8ZonGvVC',as.is=T, header=T)  # 01a_data.txt
# mydata <- read.table('data.txt',as.is=T, header=T)

# (1) First, there are different approaches to split the data (partition the data) into 
# training and testing sets.
## TRAINING: 75% of the sample size
sample_size <- floor(0.75 * nrow(mydata))
## set the seed to make your partition reproductible
set.seed(1234)
train_ind <- sample(seq_len(nrow(mydata)), size = sample_size)
train <- mydata[train_ind, ]

# TESTING DATA
test <- mydata[-train_ind, ]

lin.mod <- lm(Weight ~ Height*Team, data=train)
predicted.values <-  predict(lin.mod, newdata=test)

....

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• SOCR Home page: http://www.socr.umich.edu

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