# 0.1.7 "ButterBall"

Lathe.jl is an all-in-one package for predictive modeling in Julia. It comes packaged with a Stats Library, Preprocessing Tools, Distributions, Machine-Learning Models, and Model Validation. Lathe features easy object-oriented programming methodologies using Julia's dispatch.

#### https://lathe.ai/

(press ] in the Julia REPL to go to Pkg REPL)

#### Latest (Get the latest stable developments):

(v1.3) pkg> add Lathe


#### Unstable (Get the latest undocumented and unstable developments):

(v1.3) pkg> add Lathe#Unstable


#### Easy To Use

Lathe brings an entirely different methodology to the Julia language. Types are created to adhere to the object-oriented programming paradigm, and syntax is akin to that of Pythonic machine-learning packages, like SkLearn.

Lathe includes many of the tools commonly used by machine-learning engineers and scientists out of the box, rather than relying on more dependencies to do so.

#### Deployable

Lathe models can be easily serialized and deployed onto production servers using Genie.jl, or a similar high-performance web-server. Lathe also has support for pipelines, meaning most pre-processing operations can be automated and performed with one easy call.

#### Fast

Lathe uses a faster methodology than most other Julia packages for machine-learning. Furthermore, the package also takes advantage of the natural ability of the language to be fast. As a result, Lathe is also faster than most similar packages for other high-level statistical programming languages.

#### Julian

Lathe is written in 100-percent pure Julia. As a result, the package often takes advantage of very Julian methods of dealing with problems, such as dispatch, macros, and syntactical expressions.

## What's Inside?

### Stats

• Distributions
• Statistical tests
• Bayesian tests
• Model validation
• Sampling
• General Statistics

• Scalers
• Encoders
• Splitters

### Models

• Pipelines
• Powerlog
• Logistic Regression
• Kmeans Clustering
• Random Forest Classifier
• Decision Tree Classifier
• Pipelines
• Linear Regression
• Linear Least square