Leveraging Experiment Lines to Data Analytics
Action implementation for transform
Action
Adjust categorical mapping
Adjust to data frame
Adjust factors
Adjust to matrix
Autoencoder - Encode
Autoencoder - Encode-decode
Categorical mapping
Decision Tree for classification
K Nearest Neighbor Classification
Majority Classification
MLP for classification
Naive Bayes Classifier
Random Forest for classification
SVM for classification
Classification Tune
classification
Clustering Tune
DBSCAN
k-means
PAM
Cluster
Clusterer
Class dal_base
DAL Learner
DAL Transform
DAL Tune
Data Sample
PCA
Evaluate
maximum curvature analysis
minimum curvature analysis
tune hyperparameters of ml model
fit dbscan model
Fit
Inverse Transform
K-fold sampling
Min-max normalization
outliers_boxplot
outliers_gaussian
Plot bar graph
Boxplot per class
Plot boxplot
Plot density per class
Plot density
Plot grouped bar
Plot histogram
Plot lollipop
Plot pie
Plot points
Plot radar
Scatter graph
Plot series
Plot stacked bar
Plot a time series chart with predictions
Plot time series chart
DAL Predict
Decision Tree for regression
knn regression
MLP for regression
Random Forest for regression
SVM for regression
Regression Tune
Regression
Sample Random
Stratified Random Sampling
selection of hyperparameters
Selection hyper parameters
Default Assign parameters
Assign parameters
Smoothing by cluster
Smoothing by Freq
Smoothing by interval
Smoothing
k-fold training and test partition object
Train-Test Partition
Transform
Z-score normalization
The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.
Useful links