New

`decission_tree()`

function to create a perfect decision tree based on a set of observations and (if selected) see the step-by-step procedure;New

`multivariate_linear_regression()`

function to generate linear equation lines that approximate the values on a set of observations and (if selected) see the step-by-step procedure;New

`polynomial_regression()`

function to generate polynomial equation lines that approximate the values on a set of observations to selected degree and (if selected) see the step-by-step procedure;New

`perceptron()`

function to calculate the weights of a perceptron and predict values on a set of observations and (if selected) see the step-by-step procedure;New

`knn()`

function to perform k-nearest neighbors classification on a set of observations and (if selected) see the step-by-step procedure;New

`print.tree_struct()`

function that prints a tree with the structure of the output of the decision_tree() function;New

`act_method()`

function that calculates the selected activation function to a given input;New

`db1rl`

data.frame with 20 observations (4 features). Values form different types of lines (linear, exponential, logarithmic, sine);New

`db_per_and`

data.frame with 8 observations (2 features). “AND” logic gate;New

`db_per_or`

data.frame with 8 observations (2 features). “OR” logic gate;New

`db_per_xor`

data.frame with 8 observations (2 features). “XOR” logic gate;New

`db_flowers`

data.frame with 25 observations (4 features) containing values about flowers;New

`db2`

data.frame with 10 observations (4 features) containing values about vehicles;New

`db3`

data.frame with 12 observations (5 features) containing values about vehicles.New

`db_tree_struct`

data.frame with 12 observations (5 features) containing values about vehicles.Initial CRAN submission.