Python 真实值与预测值的降维可视化

您可能希望考虑一个小的多重图，其中混淆矩阵的每个单元格都有一个散点图.

如果PCA不能很好地发挥作用，根据我的经验，t分布随机邻居嵌入(TSNE)通常是一个很好的 Select .

例如，对于同样具有三个预测类的虹膜数据集，它可能如下所示:

import pandas as pd
import seaborn as sns
from sklearn.manifold import TSNE

iris = sns.load_dataset('iris')

# Mock up some predictions.
iris['species_pred'] = (40 * ['setosa'] + 5 * ['versicolor'] + 5 * ['virginica']
                        + 40 * ['versicolor'] + 5 * ['setosa'] + 5 * ['virginica']
                        + 40 * ['virginica'] + 5 * ['versicolor'] + 5 * ['setosa'])

# Show confusion matrix.
pd.crosstab(iris.species, iris.species_pred)

  species_pred  setosa  versicolor  virginica
species             
setosa              40           5          5
versicolor           5          40          5
virginica            5           5         40

# Reduce features to two dimensions.
X = iris.iloc[:, :4].values
X_embedded = TSNE(n_components=2, init='random', learning_rate='auto'
                 ).fit_transform(X)
iris[['tsne_x', 'tsne_y']] = X_embedded

# Plot small multiples, corresponding to confusion matrix.
sns.set()
g = sns.FacetGrid(iris, row='species', col='species_pred', margin_titles=True)
g.map(sns.scatterplot, 'tsne_x', 'tsne_y');