https://medium.com/@thongonary/how-to-compute-f1-score-for-each-epoch-in-keras-a1acd17715a2 https://datascience.stackexchange.com/questions/13746/how-to-define-a-custom-performance-metric-in-keras/20192 In training a neural network, f1 score is an im…
F1 score 关于精准率和召回率 精准率和召回率可以很好的评价对于数据极度偏斜的二分类问题的算法,有个问题,毕竟是两个指标,有的时候这两个指标也会产生差异,对于不同的算法,精准率可能高一些,召回率可能低一些,反之一样,真正使用的时候应该根据具体的使用场景来去解读这两个指标 想要得到这两个指标之间的平衡,希望能同时照顾到精准率和召回率,这样就可以使用新的指标,F1 score F1 score的目的就是兼顾这两个指标,描述的是精准率和召回率的调和平均值,公式可以写成 这种的好处就是,如果说一个…
import numpy as np import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model.logistic import LogisticRegression from sklearn.model_selection import train_test_split, cross_val_score from sklearn.metrics…
7 Machine Learning System Design Content 7 Machine Learning System Design 7.1 Prioritizing What to Work On 7.2 Error Analysis 7.3 Error Metrics for Skewed Classed 7.3.1 Precision/Recall 7.3.2 Trading off precision and recall: F1 Score 7.4 Data for ma…
8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset by Jason Brownlee on August 19, 2015 in Machine Learning Process Has this happened to you? You are working on your dataset. You create a classification model and get 90% accuracy…
Learning from Imbalanced Classes AUGUST 25TH, 2016 If you’re fresh from a machine learning course, chances are most of the datasets you used were fairly easy. Among other things, when you built classifiers, the example classes werebalanced, meaning t…
发现自己不写总结真是件很恶劣的事情,好多学的东西没有自己总结都忘记了.所以决定从今天开始,学东西的时候一定跟上总结. 我写的东西大多数是自己通俗的总结,不太喜欢写严格的定义或者证明,写了也记不住,欢迎指正. 1. High Bias vs. High Variance High Bias:通常是因为模型过于简单,使得不能成功拟合数据.比如说一些有二次曲线特性的数据,如果用一次直线去拟合就会出现这个问题,所以它对应了Underfitting问题.另外,从泛化角度来说,这样的模型泛化程度更高. Hi…
Seven Steps to Success Machine Learning in Practice Project failures in IT are all too common. The risks are higher if you are adopting a new technology that is unfamiliar to your organisation. Machine learning has been around for a long time in acad…
Suppose you have trained a logistic regression classifier which is outputing hθ(x). Currently, you predict 1 if hθ(x)≥threshold, and predict 0 if hθ(x)<threshold, where currently the threshold is set to 0.5. Suppose you increase the threshold to 0.7.…
