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.