Loan = pd.read_csv('./input/preprocessed-lending-club-dataset-v2/mycsvfile.csv', low_memory=True) Let’s load the dataset, and fit the model: from sklearn.model_selection import train_test_splitįrom ee import DecisionTreeClassifier Read and review feature importance by utilizing the SHAP library.In this article, we will use, sklearn, graphviz and shap as explainability components. What tools do i use to arrive global and local explainability?.What is global and local explainability? What is feature importance?.What is the difference between interpretability and explainability?.We will focus on practical aspects of decision trees explainability, however if you haven’t come across this topc before, i suggest you reading my previous article, where we answered the following questions: It is an essential task in data science to build an ML model that can make high-quality predictions yet be able to interpret such predictions. One way to understand and evaluate the model is to use metrics like accuracy, yet another way to do it is to use model explainability. However, we often fail to explain or understand what signal model relies on most to make the decision. They produce highly accurate predictions. Machine learning models are frequently named “black boxes”. In this article will try to “understand” how our model decision works and what packages can help us to answer how the model decides while predicting an outcome. Once you build a decision trees model, the advantage of the model is its explainability and simplicity of it. Subsequently, we discussed what is overfitting and how to handle it in this article. In the Decision Trees - How it works for Fintech article, we built a Decision Trees model and discovered that our model is overfitted. Hereyou can find the complete end-to-end data science project for beginners to learn data science. This article is part of a series where we walk step by step through solving fintech problems with different Machine Learning techniques using the “All lending club loan” dataset. This article will try to “understand” how our model decision works and what packages can help us to answer how the model decides while predicting an outcome.
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