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Categorical Data

Numerical Data

Datasets

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Analytical Goals

Upcoming Features

Gain Overview

1. Feature Selection

Feature Selection Method:

Manual Features

Number of Features

Metric

Weight Mode Euclidean

T

Weight Mode Cosine

Number Of Neighbors

Number Of Clusters

2. Dimensionality Reduction

Dimensionality Reduction Method:

Number of Features

Random State

Number of Neighbors

Minimum Distance

Metric

Power Minkowski

Perplexity

Metric

Learning Rate

3. Pattern Recognition Type

Pattern Recognition Type:

Label

4. Pattern Recognition

Classification Method:

Random State

Kernel

C Regularisation

Degree

Coefficient0 Polynomial

Coefficient0 Sigmoid

Number Of Trees

Maximum Depth of Trees

Regression Method:

Kernel

C Regularisation

Degree

Coefficient0 Polynomial

Coefficient0 Sigmoid

Random State

Number Of Trees

Maximum Depth of Trees

Random State

Clustering Method:

Random State

Number Of Clusters

Number Of Algorithm Runs

Covariance Type

Metric

Power Minkowski

Neighborhood Defining Epsilon

Minimum Samples For Core Point

5. Visualization

Analytical Workflows

Number of Recommendations:

Recommended Workflows

Feature Selection	Dimensionality Reduction	Pattern Recognition Type	Machine Learning Algorithm	Visualization	Like

Customized Workflows

Feature Selection	Dimensionality Reduction	Pattern Recognition Type	Machine Learning Algorithm	Visualization	Like

Analyze Outliers

1. Outlier Detection

Method:

Outliers Fraction

Number Of Clusters

Alpha

Beta

Number Of Trees

Maximum Samples

Maximum Features

Number Of Neighbors

Method

Power Minkowski

Nu

Kernel

Degree

Coefficient0

Gamma

Hidden Neurons

Encoder Neurons

Decoder Neurons

Activation Function Of Hidden Layers

Output Activation

Optimizer

Epochs

Batch Size

Dropout Rate

L2 Regularizer

Validation Size

Random State

2. Dimension Reduction

Method:

Random State MDS

Number Of Neighbors Isomap

Power Minkowski

Random State PCA

Number Of Neighbors MLLE

Random State LLE

3. Visualization

Analytical Workflows

Number of Recommendations:

Recommended Workflows

Outlier Detection	Dimension Reduction	Visualization	Like

Customized Workflows

Outlier Detection	Dimension Reduction	Visualization	Like

VisPicker

Analytical Goals

Upcoming Features

Gain Overview

1. Feature Selection

Feature Selection Method:

2. Dimensionality Reduction

Dimensionality Reduction Method:

3. Pattern Recognition Type

Pattern Recognition Type:

4. Pattern Recognition

Classification Method:

Regression Method:

Clustering Method:

5. Visualization

Analytical Workflows

Number of Recommendations:

Recommended Workflows

Feature Selection	Dimensionality Reduction	Pattern Recognition Type	Machine Learning Algorithm	Visualization	Like

Customized Workflows

Feature Selection	Dimensionality Reduction	Pattern Recognition Type	Machine Learning Algorithm	Visualization	Like

Analyze Outliers

1. Outlier Detection

Method:

2. Dimension Reduction

Method:

3. Visualization

Analytical Workflows

Number of Recommendations:

Recommended Workflows

Outlier Detection	Dimension Reduction	Visualization	Like

Customized Workflows

Outlier Detection	Dimension Reduction	Visualization	Like