In the research about machine learning, classification is the problem of identifying to which of a set of categories a new observation belongs, on the basis of a training set of data containing observations whose category membership is known. In this study, the procedure and principle of ...
tools for machine learning ; experience is important 2.supervised learning “right answers”given supervised learning:数据集中的每个数据都是正确的答案 Regression Question : predict continuous valued output (Regression Question) key : predict ;continuous data;回归问题 Classification Problem: discrete va...
Thus, to sum it up, while trying to resolve specific business challenges with imbalanced data sets, the classifiers produced by standard machine learning algorithms might not give accurate results. Apart from fraudulent transactions, other examples of a common business problem with imbalanced dataset ar...
在Machine Learning的Regression Problem中,常用Quadratic Function来做Cost Function,用以表征Hypothesis与Y之间的差距。而通过Gradient Descent来不断调整参数,从而缩小这个Gap从而训练我们的算法。 而在Neural Network的Classification Problem中,如果依然使用Quadratic Function,则会出现学习速率过慢的问题,这时我们就需要选用Cr...
We know that error can be composited from bias and variance. A too complex model has lowbiasbut large variance, while a too simple model has low variance but large bias, both leading a high error but two different reasons. As a result, two different ways to solve the problem come into ...
Classification is a supervised machine learning process that predicts the class of input data based on the algorithms training data. Here’s what you need to know.
To avoid this problem, we usually use logistic function to model : By fitting a model using the function, we can find that the line is coverted into a s-shaped curve. For balances close to zero, the probability will be close to zero but never below it, while for large balances, the...
To solve the problem of imbalanced classes there are many ways, not just oversampling, you can generate artificial data, add class weights in the loss function, use active learning to gather new data, use models that returns uncertainty score for each prediction (like bayesian netwo...
These generally attempt to solve the problem of low-density rejection (i.e., rejecting points that fall in areas where the training data has low probability). See here for some theory, it also references various technical approaches and surveys, although this is an area of...
Actually, I haven't tried this problem, but I had to write a proposal about this kind of task, and had to argue a lot with my CEO for my idea's feasibility.(though the point of the argument was not about the feasibility, but his misunderstanding of deep learning basics...)