PyOD is an awesome outlier detection library. In this article learn what is outlier and how to use PyOD library for outlier detection in Python.
Here is a simple example of Principal Component Analysis in Python where we perform dimension reduction on the Iris dataset withScikit-learn. Read our in-depth tutorial showingPCA Python Examples. Enjoyed This Post? Jean-Christophe Chouinard
heatmap(cm, annot=True, fmt='d', cmap='Greens') plt.title('Confusion Matrix') plt.ylabel('True label') plt.xlabel('Predicted label') plt.show() Powered By This is the output: Random Forest Confusion Matrix Output Tada 🎉 You have successfully created your first Confusion Matrix ...
import seaborn as snsimport matplotlib.pyplot as pltcorrmat = data[columns].corr()mask= np.zeros_like(corrmat)mask[np.triu_indices_from(mask)] = Truesns.heatmap(corrmat,vmax=1, vmin=-1,annot=True, annot_kws={'fontsize':7},mask=mask,cmap=sns.diverging_palette(20,220,as_cmap=True)...
Given below is a simple example code for one of the unsupervised learning techniques. Let’s use the K-Means clustering algorithm as an example. For this, we’ll use the popular Python library scikit-learn. Make sure you have it installed using“pip install scikit-learn” ...
Change the color map: sns.heatmap(x, cmap='YlGnBu') Add a color bar: plt.colorbar() Change the font style: plt.rcParams.update({'font.family': 'serif'}) Conclusion Data science visualization is a critical component of data science that enables us to effectively communicate complex data ...
for ax, interp_method in zip(axes.flat, methods): ax.imshow(testpattern , interpolation=interp_method, cmap='gray') ax.set_title(interp_method) plt.show() The error that we get looks as in the below image:- Image representing “iopub data rate exceeded” error ...
In order to retrieve the right glyph from the font to display "a", you need to consult the table inside the font (the <literal>cmap</literal> table) that maps Unicode codepoints to glyph IDs. In other words, <emphasis>text shaping turns codepoints into glyph IDs</emphasis>....
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