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, Reading 3: Machine Learning
SECRET SAUCE
Supervised Machine Learning, Unsupervised Machine Learning, and Deep Learning
The goal of machine learning is to use data to automate decision-making.
Supervised learning. Inputs and outputs are identified for the computer, and the algorithm uses this labeled training
data to model relationships.
Unsupervised learning. The computer is not given labeled data; rather, it is provided unlabeled data that the algorithm
uses to determine the structure of the data.
Deep learning algorithms. Algorithms such as neural networks and reinforced learning learn from their own prediction
errors and are used for complex tasks such as image recognition and natural language processing.
Overfitting and Methods of Addressing It
In supervised learning, overfitting results from having a large number of independent variables (features), resulting in an overly
complex model which may have generalized random noise that improves in-sample forecasting accuracy. However, overfit
models do not generalize well to new data (i.e., low out-of-sample R-squared).
To reduce the problem of overfitting, data scientists use complexity reduction and cross validation. In complexity reduction,
a penalty is imposed to exclude features that are not meaningfully contributing to out-of-sample prediction accuracy. This
penalty value increases with the number of independent variables used by the model.
Supervised Machine Learning Algorithms
Supervised learning algorithms include the following:
1. Penalized regression. This reduces overfitting by imposing a penalty on—and reducing—the nonperforming features.
2. Support vector machine. This is a linear classification algorithm that separates the data into one of two possible
classifiers based on a model-defined hyperplane.
3. K-nearest neighbor. This is used to classify an observation based on nearness to the observations in the training sample.
4. Classification and regression tree. This is used for classifying categorical target variables when there are significant
nonlinear relationships among variables.
pruningnonpredictive section
5. Ensemble learning. This combines predictions from multiple models, resulting in a lower average error rate.
6. Random forest. This is a variant of the classification tree whereby a large number of classification trees are trained using
data bagged from the same data set.
Unsupervised Machine Learning Algorithms
Unsupervised learning algorithms include the following:
1. Principal components analysis. This summarizes the information in a large number of correlated factors into a much
smaller set of uncorrelated factors called eigenvectors.
2. K-means clustering. This partitions observations into k non-overlapping clusters; a centroid is associated with each
cluster.
3. Hierarchical clustering. This builds a hierarchy of clusters without any predefined number of clusters.
Neural Networks, Deep Learning Nets, and Reinforcement Learning
Neural networks comprise an input layer, hidden layers (which process the input), and an output layer. The nodes in the
hidden layer are called neurons, which comprise a summation operator (that calculates a weighted average) and an activation
function (a nonlinear function).
Deep learning nets are neural networks with multiple hidden layers, useful for pattern, speech, and image recognition.
Reinforcement learning agents seek to learn from their own errors maximizing a defined reward.
Prev Next
Preprocessing textcleaning removing fillecharacters
normalisation
lowering removing fille words
https://www.kaplanlearn.com/education/dashboard/index/603a30d5be4d18486a42cd599e7973d1/course/108321434/node/5443265 17/08/2025, 10:20
Page 1 of 2
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OUTLIERS
arragetannold 1H potto
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ERROR
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sample
canpredictfutureÉI
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correetby as Emmen diff.scmyErmse
, Reading 3: Machine Learning
SECRET SAUCE
Supervised Machine Learning, Unsupervised Machine Learning, and Deep Learning
The goal of machine learning is to use data to automate decision-making.
Supervised learning. Inputs and outputs are identified for the computer, and the algorithm uses this labeled training
data to model relationships.
Unsupervised learning. The computer is not given labeled data; rather, it is provided unlabeled data that the algorithm
uses to determine the structure of the data.
Deep learning algorithms. Algorithms such as neural networks and reinforced learning learn from their own prediction
errors and are used for complex tasks such as image recognition and natural language processing.
Overfitting and Methods of Addressing It
In supervised learning, overfitting results from having a large number of independent variables (features), resulting in an overly
complex model which may have generalized random noise that improves in-sample forecasting accuracy. However, overfit
models do not generalize well to new data (i.e., low out-of-sample R-squared).
To reduce the problem of overfitting, data scientists use complexity reduction and cross validation. In complexity reduction,
a penalty is imposed to exclude features that are not meaningfully contributing to out-of-sample prediction accuracy. This
penalty value increases with the number of independent variables used by the model.
Supervised Machine Learning Algorithms
Supervised learning algorithms include the following:
1. Penalized regression. This reduces overfitting by imposing a penalty on—and reducing—the nonperforming features.
2. Support vector machine. This is a linear classification algorithm that separates the data into one of two possible
classifiers based on a model-defined hyperplane.
3. K-nearest neighbor. This is used to classify an observation based on nearness to the observations in the training sample.
4. Classification and regression tree. This is used for classifying categorical target variables when there are significant
nonlinear relationships among variables.
pruningnonpredictive section
5. Ensemble learning. This combines predictions from multiple models, resulting in a lower average error rate.
6. Random forest. This is a variant of the classification tree whereby a large number of classification trees are trained using
data bagged from the same data set.
Unsupervised Machine Learning Algorithms
Unsupervised learning algorithms include the following:
1. Principal components analysis. This summarizes the information in a large number of correlated factors into a much
smaller set of uncorrelated factors called eigenvectors.
2. K-means clustering. This partitions observations into k non-overlapping clusters; a centroid is associated with each
cluster.
3. Hierarchical clustering. This builds a hierarchy of clusters without any predefined number of clusters.
Neural Networks, Deep Learning Nets, and Reinforcement Learning
Neural networks comprise an input layer, hidden layers (which process the input), and an output layer. The nodes in the
hidden layer are called neurons, which comprise a summation operator (that calculates a weighted average) and an activation
function (a nonlinear function).
Deep learning nets are neural networks with multiple hidden layers, useful for pattern, speech, and image recognition.
Reinforcement learning agents seek to learn from their own errors maximizing a defined reward.
Prev Next
Preprocessing textcleaning removing fillecharacters
normalisation
lowering removing fille words
https://www.kaplanlearn.com/education/dashboard/index/603a30d5be4d18486a42cd599e7973d1/course/108321434/node/5443265 17/08/2025, 10:20
Page 1 of 2
, Parities
parenting
mile EE e
ALWAYS
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ofsettlements
MundellFleming
iiii i
at sentimetana
iiiin.iniiiiiiiiii
it.EE um
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