| Name | Age | Class/Dept | Fare | Joined | Job | Survived | |
|---|---|---|---|---|---|---|---|
| ALLEN, Miss Elisabeth Walton | 29 | 1st Class | 16,300 | Southampton | TRUE | ||
| ALLISON, Master Hudson Trevor | 0.9 | 1st Class | 11,700 | Southampton | TRUE | ||
| ALLISON, Miss Helen Loraine | 2 | 1st Class | 11,700 | Southampton | FALSE | ||
| ALLISON, Mr Hudson Joshua Creighton | 30 | 1st Class | 11,700 | Southampton | Businessman | FALSE | |
| ALLISON, Mrs Bessie Waldo | 25 | 1st Class | 11,700 | Southampton | FALSE | ||
| ANDERSON, Mr Harry | 47 | 1st Class | 2,050 | Southampton | Stockbroker | TRUE | |
| ANDREWS, Miss Kornelia Theodosia | 62 | 1st Class | 6,020 | Cherbourg | TRUE | ||
| ANDREWS, Mr Thomas | 39 | 1st Class | Belfast | Shipbuilder | FALSE | ||
| APPLETON, Mrs Charlotte | 53 | 1st Class | 3,980 | Southampton | TRUE |
An icicle mapping of a BigML decision tree built on the iris dataset.
The top row represents the root of the tree. Each lower row contains the children of the upper row's nodes. The number of training instances captured by a node determine its width.
An icicle mapping of a BigML decision tree built on the Pima diabetes dataset.
The top row represents the root of the tree. Each lower row contains the children of the upper row's nodes. The number of training instances captured by a node determine its width.
An icicle mapping of a BigML decision tree built on the concrete compressive strength dataset.
The top row represents the root of the tree. Each lower row contains the children of the upper row's nodes. The number of training instances captured by a node determine its width.
A tree mapping of a BigML decision tree built on the iris dataset.
The outer most rectangle represents the root of the tree. Each inner retangle represents the children of the outer rectangle's nodes. The number of training instances captured by a node determine its size.
A tree mapping of a BigML decision tree built on the Pima diabetes dataset.
The outer most rectangle represents the root of the tree. Each inner retangle represents the children of the outer rectangle's nodes. The number of training instances captured by a node determine its size.
A tree mapping of a BigML decision tree built on the concrete compressive strength dataset.
The outer most rectangle represents the root of the tree. Each inner retangle represents the children of the outer rectangle's nodes. The number of training instances captured by a node determine its size.
A visualization of five clusters discovered on four fields from the Pima Indian Diabetes dataset.
Each cluster is represented by a ball. The cluster radii are proportional to the population of each cluster.
The y-axis shows the distance of each cluster to the current point (selected by the sliders). The lower a cluster's position, the nearer it is the current point. The order of the clusters on the x-axis is from nearest to furthest.
The initial point is the median for each field. Selecting a cluster will set the current point equal to the cluster's centroid.
Finally, the colors on each slider represent the closest cluster to the current point for that range.
A visualization of three clusters discovered on the Iris dataset.
Each cluster is represented by a ball. The cluster radii are proportional to the population of each cluster.
The y-axis shows the distance of each cluster to the current point (selected by the sliders). The lower a cluster's position, the nearer it is the current point. The order of the clusters on the x-axis is from nearest to furthest.
The initial point is the median for each field. Selecting a cluster will set the current point equal to the cluster's centroid.
Finally, the colors on each slider represent the closest cluster to the current point for that range.
A visualization of seven clusters discovered on the wine quality dataset.
Each cluster is represented by a ball. The cluster radii are proportional to the population of each cluster.
The y-axis shows the distance of each cluster to the current point (selected by the sliders). The lower a cluster's position, the nearer it is the current point. The order of the clusters on the x-axis is from nearest to furthest.
The initial point is the median for each field. Selecting a cluster will set the current point equal to the cluster's centroid.
Finally, the colors on each slider represent the closest cluster to the current point for that range.