How to plot Confusion matrix and ROC for my own... Learn more about confusion matrix, roc, plot, own data set
I'm building a Random Forest Binary Classsifier in python on a pre-processed dataset with 4898 instances, 60-40 stratified split-ratio and 78% data belonging to one target label and the rest to the other. What value of n_estimators should I choose in order to achieve the most practically...
In the near future, we can expect Artificial Intelligence (AI) models to take over decision-making tasks such as lawsuits, non-urgent patient care, or screenwriting. But before we get there, we need to thoroughly understand how and why a decision was reached. Unlike humans,machine learning mo...
A Receiver Operating Characteristics (ROC) curve is a plot of the “true positive rate” with respect to the “false positive rate” at different threshold settings. ROC curves are usually defined for a binary classification model, although that can be extended to a multi-class setting, which ...
In this article I explain the core of the SVMs, why and how to use them. Additionally, I show how to plot the support… towardsdatascience.com Everything you need to know about Min-Max normalization in Python In this post I explain what Min-Max scaling is, ...
The ROC curve I got from each fold is with different size. In addition, I want to plot the mean ROC of these ten ROC curves I got from the cross-validation. Anyone knows how to do this? I read another post using Python perfectly solve the problem using 1D interpolation. Not sure ho...
The goal of this post is to explain what the Lift curve in Machine Learning is, how it can complement other classification evaluation techniques like the ROC curve, and how it can be used to compare different models.It complements our previous postsThe Confusion Matrix in PythonandROC in Machi...
How to Use Metrics for Deep Learning With Keras in Python This can be technically challenging. A much simpler alternative is to use your final model to make a prediction for the test dataset, then calculate any metric you wish using the scikit-learn metrics API. Three metrics, in ad...
Specifically, you can change the fontsize parameter in the heatmap function call on line 74. The default value is 14; you can increase it to the desired size. However, please note that while increasing the font size may make the numbers in the plot more readable, it may also reduce the...
ROC Curve provides a comprehensive visual representation of a classifier's performance at all thresholds, letting analysts choose a threshold that balances sensitivity and specificity according to the business context. Lift Curve focuses more on the effectiveness of a predictive model in terms of "lifti...