%matplotlib inline
A matplotlib utility function for visualizing results from feature_selection.SequentialFeatureSelector
.
from mlxtend.plotting import plot_sequential_feature_selection
for more information on sequential feature selection, please see feature_selection.SequentialFeatureSelector
.
from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
knn = KNeighborsClassifier(n_neighbors=4)
sfs = SFS(knn,
k_features=4,
forward=True,
floating=False,
scoring='accuracy',
cv=5)
sfs = sfs.fit(X, y)
fig1 = plot_sfs(sfs.get_metric_dict(), kind='std_dev')
plt.ylim([0.8, 1])
plt.title('Sequential Forward Selection (w. StdDev)')
plt.grid()
plt.show()
Features: 4/4
with open('../../api_modules/mlxtend.plotting/plot_sequential_feature_selection.md', 'r') as f:
s = f.read()
print(s)
## plot_sequential_feature_selection *plot_sequential_feature_selection(metric_dict, kind='std_dev', color='blue', bcolor='steelblue', marker='o', alpha=0.2, ylabel='Performance', confidence_interval=0.95)* Plot feature selection results. **Parameters** - `metric_dict` : mlxtend.SequentialFeatureSelector.get_metric_dict() object - `kind` : str (default: "std_dev") The kind of error bar or confidence interval in {'std_dev', 'std_err', 'ci', None}. - `color` : str (default: "blue") Color of the lineplot (accepts any matplotlib color name) - `bcolor` : str (default: "steelblue"). Color of the error bars / confidence intervals (accepts any matplotlib color name). - `marker` : str (default: "o") Marker of the line plot (accepts any matplotlib marker name). - `alpha` : float in [0, 1] (default: 0.2) Transparency of the error bars / confidence intervals. - `ylabel` : str (default: "Performance") Y-axis label. - `confidence_interval` : float (default: 0.95) Confidence level if `kind='ci'`. **Returns** - `fig` : matplotlib.pyplot.figure() object **Examples** For usage examples, please see [http://rasbt.github.io/mlxtend/user_guide/plotting/plot_sequential_feature_selection/](http://rasbt.github.io/mlxtend/user_guide/plotting/plot_sequential_feature_selection/)