classification,

linear classifier (a binary classifier)

soft margin classification

Application: particularly suited for small- to medium-size but complex high-dimensional data sets, such as corporate financial statements or bankruptcy databases. Investors seek to predict company failures for identifying stocks to avoid or to short sell, and SVM can generate a binary classification (e.g., bankruptcy likely vs. bankruptcy unlikely) using many fundamental and technical feature variables.

look at the diamond’s k nearest neighbors

A critical challenge of KNN is defining what it means to be “similar” (or near).

Besides the selection of features, an important decision relates to the distance metric used to model similarity because an inappropriate measure will generate poorly performing models.

Applications: including bankruptcy prediction, stock price prediction, corporate bond credit rating assignment, and customized equity and bond index creation.

CART is applied to binary classification or regression.

Application: enhancing detection of fraud in financial statements, generating consistent decision processes in equity and fixed-income selection, and simplifying communication of investment strategies to clients.

Classification

Example

PCA is used to summarize or reduce highly correlated features of data into a few main, uncorrelated composite variables.

A composite variable is a variable that combines two or more variables that are statistically strongly related to each other

two key concepts: eigenvectors and eigenvalues

The eigenvectors define new, mutually uncorrelated composite variables that are linear combinations of the original features.

An eigenvalue gives the proportion of total variance in the initial data that is explained by each eigenvector.

black box

Application:. It is typically performed as part of exploratory data analysis, before training another supervised or unsupervised learning model.

K-means is a relatively old algorithm that repeatedly partitions observations into a fixed number, k, of non-overlapping clusters

Application: in data exploration for discovering patterns in high dimensional data or as a method for deriving alternatives to existing static industry classifications.

agglomerative clustering (or bottom-up)

clustering (or top-down)

4: input layer

5:hidden layers

1: output layer

A functional part of a neural network’s node that multiplies each input value received by a weight and sums the weighted values ​​to form the total net input, which is then passed to the activation function.

A functional part of a neural network’s node that transforms the total net input received into the final output of the node. The activation function operates like a light dimmer switch that decreases or increases the strength of the input.

The process of adjusting weights in a neural network, to reduce total error of the network, by moving forward through the network’s layers.

The process of adjusting weights in a neural network, to reduce total error of the network, by moving backward through the network’s layers.

A parameter that affects the magnitude of adjustments in the weights in a neural network.