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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.