| Model Type | Model Name |

|:––––– | ––––– |

| Baseline | MeanBaseline |

| Baseline | ClassBaseline |

| Continuous | LinearRegression |

| Continuous | LinearLeastSquare |

| Categorical | RandomForestClassifier |

| Categorical | DecisionTree |

| Tools | Pipeline |

| Tools | PowerLog |

Linear Regression is a well-known linear function used for predicting continuous features with a mostly linear or semi-linear slope.

==PARAMETERS==

[y] <- Fill with your trainY values. Should be an array of shape (0,1) or (1,0)

[x] <- Fill in with your trainX values. Should be an array of shape (0,1) or (1,0)

==Functions==

predict(xt) <- Returns a prediction from the model based on the xtrain value passed (xt)

Pipeline

Description

Rescales an array. Pipelines can contain a predictable Lathe model with preprocessing that occurs automatically. This is done by putting X array processing methods into the iterable steps, and then putting your Lathe model in.

Input

Positional Arguments

LatheObject :: steps - An infinte argument of LatheObject types. These types are any Lathe model or preprocessor.

Output

Pipeline :: A Pipeline object.

Functions

Pipeline.predict(xt) :: Applies the steps inside of the pipeline to xt.

Data

steps - An array of LatheObject types that are predicted with usng the predict() function call.

Methods

Base.+ - The + operator can be used to add steps to a pipeline.

Pipeline + LatheObject

Base.- - The - operator can be used to remove steps from a pipeline.

Pipeline - Int64(Position in steps)

Random Forest Classifier

Description

The Random Forest Classifier uses a multitude of decision trees to solve classification problems.

Input

RandomForestClassifier(x, y, rng ; maxdepth = 6, minnoderecords = 1, ntrees = 100)

Positional Arguments

Array{Any} - X:: Array of x's for which the model will use to predict y.

Array{Any} - Y:: Array of y's for which the x's are used to predict.

RandomNumberGenerator - rng:: Determines the seed for the given model.

Key-word Arguments

Int64 - max_depth:: Determines the max depth which the tree should use as a stop parameter.

Int64 - minnoderecords:: Determines the minimum number of nodes a constructed node is allowed to have.

Int64 - n_trees:: Determines how many decision trees should be trained.

Output

model:: A Lathe Model.

Functions

Model.predict(xt) :: Predicts a new y based on the data provided as xt and the weights obtained from X.

Data

storedata :: A tree node type that contains the weights and their corresponding values.

Decision Tree Classifier

Description

The decision tree classifier is a model ideal for solving most classification problems.

Input

DecisionTreeClassifier(x, y, rng ; maxdepth = 6, minnode_records = 1)

Positional Arguments

Array{Any} - X:: Array of x's for which the model will use to predict y.

Array{Any} - Y:: Array of y's for which the x's are used to predict.

RandomNumberGenerator - rng:: Determines the seed for the given model.

Key-word Arguments

Int64 - max_depth:: Determines the max depth which the tree should use as a stop parameter.

Int64 - minnoderecords:: Determines the minimum number of nodes a constructed node is allowed to have.

Output

model:: A Lathe Model.

Functions

Model.predict(xt) :: Predicts a new y based on the data provided as xt and the weightsz obtained from X.

Data

storedata :: A tree node type that contains the weights and their corresponding values.

Least Square regressors are ideal for predicting continous features.

x = [7,6,5,6,5]

y = [3.4.5.6.3]

xtrain = [7,5,4,5,3,5,7,8]

Type = :LIN

model = models.LeastSquare(x,y,Type)

y_pred = models.predict(model,xtrain)

HYPER PARAMETERS

==Functions==

predict(xt) <- Returns a prediction from the model based on the xtrain value passed (xt)

Majority class baseline is used to find the most often interpreted classification in an array.

==PARAMETERS==

[X] <- Fill with your trainX values. Should be an array of shape (0,1) or (1,0)

[y] <- Fill with your trainy values. Should be an array of shape (0,1) or (1,0)

λ = .0001 <- Lambda Value

fit_intercept = true <- Boolean determines whether to fit an intercept.

max_iter = 1000 <- Determines the maximum number of iterations for the model to perform.

==Functions==

predict(xt) <- Returns a prediction from the model based on the xtrain value passed (xt)

A mean baseline is great for getting a basic accuracy score in order to make a valid direction for your model.

==PARAMETERS==

[y] <- Fill with your trainY values. Should be an array of shape (0,1) or (1,0)

pipl = Pipeline([StandardScalar(),LinearRegression(trainX,trainy)])

==Functions==

predict(xt) <- Returns a prediction from the model based on the xtrain value passed (xt)

A powerlog can be used to perform a one-tailed test, as well as get the proper sample size for a testing population.

==PARAMETERS==

p1 <- A Float64 percentage representing the probability of scenario one.

p2 <- A Float64 percentage representing the probability of scenario two. These two probability values should follow these guidelines: p1 = p1 + x = p2

alpha = 0.05 <- Sets an alpha value

Returns power, sample_size

Majority class baseline is used to find the most often interpreted classification in an array.

==PARAMETERS==

[y] <- Fill with your trainY values. Should be an array of shape (0,1) or (1,0)

==Functions==

predict(xt) <- Returns a prediction from the model based on the xtrain value passed (xt)

counts() <- Returns a dictionary with the counts of all inserted keys.

highest() <- Will return a Dictionary key with the count as well as the value for the most interpreted classification.

feature_best_split

For a given feature search best split value.