supple jacinth
@light rain I trained a decision tree model in python and then plotted its feature_importances as a pie chart. The model was fed 16 features. The image shows the pie chart I got. It has overwritten feature names, and the numbers in the sectors dont have "%". How do I make this pie chart look better? This was my code : plt.pie(fdtree.feature_importances_,labels=df.columns[:-1],autopct='%.2f')
plt.title("Feature Importances/Contributions")
plt.show()
light rainBOT
<@1082069574901563453> I trained a decision tree model in python and then plotte...
Calling function vision...
{"url":"https://cdn.discordapp.com/attachments/1176153760553238589/1176153765565448322/dLEN5kolaLIAiCIAjlRox4CIIgCIJQbkTgIQiCIAhCuRGBhyAIgiAI5UYEHoIgCIIglBsReAiCIAiCUG5E4CEIgiAIQrkRgYcgCIIgCOVGBB6CIAiCIJQbEXgIgiAIglBuROAhCIIgCEK5EYGHIAiCIAjlRgQegiAIgiCUGxF4CIIgCIJQbkTgIQiCIAhCuRGBhyAIgiAI5UYEHoIgCIIglBsReAiCIAiCUG5E4CEIgiAIQrkRgYcgCIIgCOVGBB6CIAiCIJQbEXgIgiAIglBuROAhCIIgCEK5EYGHIAiCIAjlRgQegiAIgiCUGxF4CIIgCIJQbkTgIQiCIAhCuRGBhyAIgiAI5UYEHoIgCIIglBsReAiCIAiCUG7H1fxVU9SblRAAAAAElFTkSuQmCC.png"}
Finished calling function vision.
Thank you for providing the image. I understand you're having issues with overlapping labels and missing percentage signs in your pie chart.
Here's what you can do to improve the appearance of the pie chart:
% Sign: Modify the autopct parameter to include a percent sign.Here's an example code snippet implementing these suggestions:
import matplotlib.pyplot as plt
# Data preparation
features = fdtree.feature_importances_
feature_names = df.columns[:-1]
explode = [0.1 if f == max(features) else 0 for f in features] # Explode the largest sector
# Pie chart, where the slices will be ordered and plotted counter-clockwise:
fig, ax = plt.subplots(figsize=(10, 6)) # You can adjust the size as necessary
ax.pie(features, labels=feature_names, autopct='%.2f%%', startangle=90, explode=explode)
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.title("Feature Importances/Contributions")
plt.legend(loc='upper right', bbox_to_anchor=(1.35, 1.025)) # Place the legend outside of the plot
plt.tight_layout() # Adjusts plot parameters so that the figure fits into the subplot area
plt.show()
This code will create a larger pie chart with the largest feature slightly exploded, and a legend placed outside of the plot area to prevent overlapping.
Please adjust the values for `explode`, `figsize`, and `bbox_to_anchor` to best fit your specific chart.
Try running this adjusted code and check if it improves the visualization of your pie chart. If you still have issues or need further customization, let me know, and I can offer more focused advice!@light rain I trained 2 models: decision tree regressor and knn regressor and I computed their performances using MAPE and R-squared error. Then I applied PCA on both the models and again computed their performances. The performance of knn increased but the performance of decision tree decreased. I want to visualize this like a "Before applying PCA" and "After applying PCA" for both models in single plot to compare. Give shortest code
light rainBOT
<@1082069574901563453> I trained 2 models: decision tree regressor and knn regre...
To compare the performance metrics of two models before and after applying PCA, you can use a grouped bar chart. In this chart, you'll have pairs of bars for each metric, one for the Decision Tree Regressor and one for the KNN Regressor, with each pair grouped by the 'Before PCA' and 'After PCA' condition.
Here's a short snippet of code using matplotlib to visualize the comparison. In this example, let's assume you have stored the performance metrics in variables like so:
mape_dt_before, mape_dt_after: MAPE for Decision Tree before and after PCAmape_knn_before, mape_knn_after: MAPE for KNN before and after PCAr2_dt_before, r2_dt_after: R-squared for Decision Tree before and after PCAr2_knn_before, r2_knn_after: R-squared for KNN before and after PCANow, let's plot this data:
import matplotlib.pyplot as plt
import numpy as np
# Example metric values
mape_dt_before, mape_dt_after = 20, 25
mape_knn_before, mape_knn_after = 18, 15
r2_dt_before, r2_dt_after = 0.7, 0.6
r2_knn_before, r2_knn_after = 0.65, 0.7
# Set the positions of the bars on the x-axis
indices = np.arange(2)
# Bar width
width = 0.35
fig, ax = plt.subplots()
# Plotting MAPE
ax.bar(indices, [mape_dt_before, mape_knn_before], width, label='MAPE Before PCA')
ax.bar(indices + width, [mape_dt_after, mape_knn_after], width, label='MAPE After PCA')
# Plotting R-squared
ax.bar(indices + 2*width, [r2_dt_before, r2_knn_before], width, label='R2 Before PCA')
ax.bar(indices + 3*width, [r2_dt_after, r2_knn_after], width, label='R2 After PCA')
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Scores')
ax.set_title('Model Performance Comparison')
ax.set_xticks(indices + width)
ax.set_xticklabels(['Decision Tree', 'KNN'])
ax.legend()
plt.show()
This snippet creates a grouped bar chart with different colors for the 'Before PCA' and 'After PCA' conditions. Make sure to replace the placeholder values with your actual computed metrics.
light rainBOT
To compare the performance metrics of two models before and after applying PCA, ...
Adjust the width to fit the bar groups nicely on your plot. This is the simplest way to represent the changes in the performance of different models due to PCA.
supple jacinth
@light rain now plot only mape erros, leave out r squared errors and plot on 2 subplots