stanford machine learning certificate

, or even smaller after a very small concatenation, or even roundOff 0. This will affect the judgment, so they will be transferred to the logarithm space for calculation, the logarithm is often used in machine learning, to avoid ambiguity caused by the numerical operation while keeping monotonous, in addition, the logarithm can be used to convert multiplication to addition to accelerate the operation. There

as a basis for dividing two classes. In combination with P/R analysis, the selection of threshold value can be more flexible and excellent.As you can see, if you choose a threshold that is too low, more test samples will be divided into 1 categories. Therefore, the recall rate can be improved, obviously the accuracy of the sacrifice of the corresponding accuracy rate.In this example, perhaps I would choose 0.42 as the dividing value-because the accuracy and recall rates are high.Finally, give s

In Coursera Stanford Machine Learning,lecturer strongly recommended open source programming environment octave Start, so I also downloaded to try itReference Link: http://www.linuxdiyf.com/linux/22034.html******************************************************************************Installation (Ubuntu16.04): I saw the Xia Guan Web, Ubuntu has been updated to 4.0

randomly groups the data to the extent that training intensive accounts for 70% of the original data (this ratio can vary depending on the situation), and the test error is used as the criterion when selecting the model. The question comes from the Stanford University Machine Learning course on Coursera, which is described as follows: the size and price of the

Foundataions of machine learning: Rademacher complexity and VC-dimension (2) (1) growth Function) Before introducing the growth function, let's introduce an example which will help you understand the growth function. When the input space is $ \ mathbb {r} $, assume that the space is a threshold function, that is, when the input vertex $ x> V $, The point is marked as positive. For example, Figure 1 shows th

Recently in the study "Spark machine learning this book", the book used Ipython, the machine is Redhat version, with the Python2.6.6, installation needs to upgrade more than 2.7, or will report IPython requires Python version 2.7 or 3.3 or above. This is a mistake. The following is the process of resolution. 1.Python Installation Upgrade Step 1 installation Pyht

It is mentioned in this series that using Python to start machine learning (3: Data fitting and generalized linear regression) mentions the regression algorithm for numerical prediction. The logical regression algorithm is essentially regression, but it introduces a logical function to help classify it. The practice found that the logical regression in the field of text classification performance is also ve