I tried to visualize the model with the low-code machine learning library "PyCaret"

Overview

things to do

The list is as follows, but it can be executed in a few lines by automating pycaret.

Try it (⑤ Visualization of model)

⑤ Model visualization

evaluate_model(tuned_model)

AUC (ROC curve)

Confusion Matrix

Error

Dicision Boundary

# Logistic
Regression
K Nearest
Neighbour
Gaussian
Process
boundary lr.png knn.png GP.png
Feature Because of the linear algorithm
The decision boundary is also straight
Grouping nearby points
Boundary
Be aware of the bell curve
Smooth curved surface

Threshold

Precision Recall

Learning Curve

Validation Curve

algorithm Horizontal axis algorithm Horizontal axis
Decision Tree
Random Forest
Gradient Boosting
Extra Trees Classifier
Extreme Gradient Boosting
Light Gradient Boosting
CatBoost Classifier
max_depth Logistic Regression
SVM (Linear)
SVM (RBF)
C
Multi Level Perceptron (MLP)
Ridge Classifier
alpha AdaBoost n_estimators
K Nearest Neighbour(knn) n_neighbors Gaussian Process(GP) max_iter_predict
Quadratic Disc. Analysis (QDA) reg_param Naives Bayes var_smoothing

Feature Importance

Manifold Learning

download.png

Dimensions

Try it ((1) Data load- (4) Tuning)

① Data load

from pycaret.datasets import get_data
#Load the credit dataset.
#If you specify the profile option as True, pandas-EDA by profiling runs.
data = get_data('credit',profile=False)

② Pretreatment

from pycaret.classification import *
exp1 = setup(data, target = 'default')

③ Model comparison

compare_models(sort="AUC")

④ Parameter tuning

tuned_model = tune_model(estimator='lightgbm')

tune_model_v1.gif

*The algorithms that can be specified are as follows.docstringBut you can check it.

algorithm Specifying the estimator algorithm Specifying the estimator
Logistic Regression 'lr' Random Forest 'rf'
K Nearest Neighbour 'knn' Quadratic Disc. Analysis 'qda'
Naives Bayes 'nb' AdaBoost 'ada'
Decision Tree 'dt' Gradient Boosting 'gbc'
SVM (Linear) 'svm' Linear Disc. Analysis 'lda'
SVM (RBF) 'rbfsvm' Extra Trees Classifier 'et'
Gaussian Process 'gpc' Extreme Gradient Boosting 'xgboost'
Multi Level Perceptron 'mlp' Light Gradient Boosting 'lightgbm'
Ridge Classifier 'ridge' CatBoost Classifier 'catboost'

#Summary *I have written the method of visualizing the model separately, so I would like to finish organizing it by application at the end. *Assuming input data-> modeling-> results, I would like to group them for the following 5 purposes. * A)Understand the input data and features themselves. * B)Understand the features that the model is looking at. * C)Determine the model's learning status (insufficient learning, overfitting). * D)Consider the predictive characteristics of the model and the thresholds at which the objectives can be achieved. * E)Understand the prediction performance and prediction results of the model.

Use Perspective Visualization means
A)Understand the input data and features themselves. Is positive / negative data separable? Manifold Learning download.png
Same as above Dimensions download.png
B)Understand the features that the model is looking at. Which features are important Feature Importance download.png
C)Determine the model's learning status (insufficient learning, overfitting). Can prediction performance be improved by increasing the number of learnings? Learning Curve income_lc.png
Is overfitting suppressed by regularization? Validation Curve income_vc.png
D)Consider the predictive characteristics of the model and the thresholds at which the objectives can be achieved. Which threshold value corresponds to the desired prediction characteristic? Threshold download.png
What is the relationship between Precision and Recall? Precision Recall download.png
E)Understand the prediction performance and prediction results of the model. What is the AUC (Predictive Performance)? AUC download.png
Understand the boundaries of the results Decision Boundary mc_db.png
Understand how to make mistakes Confusion Matrix mc_conf.png
Same as above Error mc_err.png

#Finally *Thank you for staying with us. *If you don't mindLike, shareI would be happy if you could. *If there is a response to some extent, I will write a masterpiece (parameter explanation, etc.).

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