SmplML 1.0.3

SmplML is a user-friendly Python library for streamlined machine learning classification. It offers intuitive functions for data preprocessing, model training, and evaluation of both classification and regression tasks. Ideal for beginners and experts alike, SmplML simplifies tasks, enabling you to gain valuable insights from your data with ease.

SmplML / SimpleML: Simplified Machine Learning for Classification

SmplML is a user-friendly Python library for streamlined machine learning classification. It offers intuitive modules for data preprocessing, model training, and evaluation. Ideal for beginners and experts alike, EasyML simplifies classification tasks, enabling you to gain valuable insights from your data with ease.

Features

• Data preprocessing: Easily handle encoding categorical variables.
• Model training: Train various classification models with just a few lines of code.
• Model evaluation: Evaluate model performance using common metrics and visualizations.

Installation

• You can install SmpML using pip:

pip install SimpleML

Usage

Classification

import seaborn as sns
import pandas as pd
from smpl_ml.smpl_ml import TrainModel

from sklearn.neighbors import KNeighborsClassifier

# Load the dataset
df = sns.load_dataset('penguins')

# Set the target and features
target = 'species'
features = df.iloc[:, df.columns != target].columns

# Create the classifier trainer
clf_trainer = TrainModel(df.dropna(), target=target, features=features, model=KNeighborsClassifier())

# Fit the model
clf_trainer.fit()

# Evaluate the model
clf_model = clf_trainer.evaluate()

model when verbose is set to True will return a DataFrame of classification metrics.

	Recall	Specificity	Precision	F1-Score	Accuracy
Adelie	0.91	0.70	0.67	0.77	0.76
Chinstrap	0.38	0.92	0.62	0.47	0.76
Gentoo	0.86	1.00	1.00	0.92	0.76

Regression

import seaborn as sns
import pandas as pd
from smpl_ml.smpl_ml import TrainModel

from sklearn.neighbors import KNeighborsRegressor

# Load the dataset
df = sns.load_dataset('iris')

# Set the target and features
target = 'sepal_length'
features = df.columns[1:]

# Create the regressor trainer
reg_trainer = TrainModel(df.dropna(), target=target, features=features, model=KNeighborsRegressor())

# Fit the model
reg_trainer.fit()

# Evaluate the model
reg_model = reg_trainer.evaluate()

	Metrics
MSE	0.11
RMSE	0.34
MAE	0.27
R-Squared	0.74

Change Log

1.0.3 (06/12/2023)

Added Regression