## How can I change the size of a Yellowbrick plot?¶

You can change the size of a plot by passing in the desired dimensions in pixels on instantiation of the visualizer:

# Import the visualizer

# Instantiate the visualizer using the size param
classes=classes, features=features, size=(1080, 720)
)

...


Note: we are considering adding support for passing in size in inches in a future Yellowbrick release. For a convenient inch-to-pixel converter, check out www.unitconversion.org.

## How can I change the title of a Yellowbrick plot?¶

You can change the title of a plot by passing in the desired title as a string on instantiation:

from yellowbrick.classifier import ROCAUC
from sklearn.linear_model import RidgeClassifier

my_title = "ROCAUC Curves for Multiclass RidgeClassifier"

# Instantiate the visualizer passing the custom title
visualizer = ROCAUC(
RidgeClassifier(), classes=classes, title=my_title
)

...


## How can I change the color of a Yellowbrick plot?¶

Yellowbrick uses colors to make visualzers as interpretable as possible for intuitive machine learning diagnostics. Generally, color is specified by the target variable, y that you might pass to an estimator’s fit method. Therefore Yellowbrick considers color based on the datatype of the target:

• Discrete: when the target is represented by discrete classes, Yellowbrick uses categorical colors that are easy to discriminate from each other.

• Continuous: when the target is represented by continous values, Yellowbrick uses a sequential colormap to show the range of data.

Most visualizers therefore accept the colors and colormap arguments when they are initialized. Generally speaking, if the target is discrete, specify colors as a list of valid matplotlib colors; otherwise if your target is continuous, specify a matplotlib colormap or colormap name. Most Yellowbrick visualizers are smart enough to figure out the colors for each of your data points based on what you pass in; for example if you pass in a colormap for a discrete target, the visualizer will create a list of discrete colors from the sequential colors.

Note

Although most visualizers support these arguments, please be sure to check the visualizer as it may have specific color requirements. E.g. the ResidualsPlot accepts the train_color, test_color, and line_color to modify its visualization. To see a visualizer’s arguments you can use help(Visualizer) or visualizer.get_params().

For example, the Manifold can visualize both discrete and sequential targets. In the discrete case, pass a list of valid color values as follows:

from yellowbrick.features.manifold import Manifold

visualizer = Manifold(
manifold="tsne", target="discrete", colors=["teal", "orchid"]
)

...


… whereas for continuous targets, it is better to specify a matplotlib colormap:

from yellowbrick.features.manifold import Manifold

visualizer = Manifold(
manifold="isomap", target="continuous", colormap="YlOrRd"
)

...


Finally please note that you can manipulate the default colors that Yellowbrick uses by modifying the matplotlib styles, particularly the default color cycle. Yellowbrick also has some tools for style management, please see Colors and Style for more information.

## How can I save a Yellowbrick plot?¶

Save your Yellowbrick plot by passing an outpath into show():

from sklearn.cluster import MiniBatchKMeans
from yellowbrick.cluster import KElbowVisualizer

visualizer = KElbowVisualizer(MiniBatchKMeans(), k=(4,12))

visualizer.fit(X)
visualizer.show(outpath="kelbow_minibatchkmeans.png")

...


Most backends support png, pdf, ps, eps and svg to save your work!

## How can I make overlapping points show up better?¶

You can use the alpha param to change the opacity of plotted points (where alpha=1 is complete opacity, and alpha=0 is complete transparency):

from yellowbrick.contrib.scatter import ScatterVisualizer

visualizer = ScatterVisualizer(
x="light", y="C02", classes=classes, alpha=0.5
)


## How can I access the sample datasets used in the examples?¶

Visit the Example Datasets page.