其中等距分段(Equval length intervals)是指分段的区间是一致的,比如年龄以十年作为一个分段;等深分段(Equal frequency intervals)是先确定分段数量,然后令每个分段中数据数量大致相等;最优分段(Optimal Binning)又叫监督离散化(supervised discretizaion),使用递归划分(Recursive Partitioning)将连续变量分为分段,背后是一...
Python中所有最流行的机器学习库都有一种称为“ predict_proba”的方法：Scikit-learn（例如LogisticRegression，SVC，RandomForest等），XGBoost，LightGBM，CatBoost，Keras…但是，尽管它的名字是预测概率，“predict_proba”并不能完全预测概率。实际上，不同的研究（尤其是这个研究和这个研究）表明，最为常见的预测模型...
信用评分卡开发中一般有常用的等距分段、等深分段、最优分段。其中等距分段(Equvallengthintervals)是指分段的区间是一致的,比如年龄以十年作为一个分段;等深分段(Equalfrequencyintervals)是先确定分段数量,然后令每个分段中数据数量大致相等;最优分段(OptimalBinning)又叫监督离散化(superviseddiscretizaion),使用递归划分...
Python package for conformal prediction Topics pythonmachine-learningscikit-learnsklearnjupyter-notebookregressionconfidence-intervalsuncertainty-quantificationquantile-regressionconformal-predictionprediction-intervalscumulative-distribution-functionprediction-setsconformal-regressorsconformal-predictive-systemsconformal-classifiers...
If it was supervised, we would have pairs of photos of the same streets, in sunny & rainy weather. But such data is hard to come by, especially in the quantities needed for deep learning. So what if you just have a bunch of sunny street photos, and a set of rainy ones? (with no...
Implementing a multiple linear regression model Summary Further reading Model Development and Evaluation Technical requirements Types of machine learning Understanding supervised learning Regression Classification Understanding unsupervised learning Applications of unsupervised learning Clustering using MiniBatch K-means...
PiML also works for arbitrary supervised ML models under regression and binary classification settings. It supports a whole spectrum of outcome testing, including but not limited to the following: Accuracy: popular metrics like MSE, MAE for regression tasks and ACC, AUC, Recall, Precision, F1-sco...
you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualisations for exploratory data analysis (EDA) to visualise unexpected values.Finally, you'll build functions and classes that you can reuse without modifi...
It may be one of the most popular techniques for structured (tabular) classification and regression predictive modeling problems given that it performs so well across a wide range of datasets in practice. A major problem of gradient boosting is that it is slow to train the model. This is part...
TheK-nearest Neighbors (KNN)algorithm is a type of supervised machine learning algorithm used for classification, regression as well as outlier detection. It is extremely easy to implement in its most basic form but can perform fairly complex tasks. It is a lazy learning algorithm since it doesn...