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Polynomial logistic regression is to add high-order terms as features on the basis of logistic regression to realize the extraction of high-dimensional features Model construction Polynomial logistic regression model is composed of three sub models: (1) Add polynomial feature (2) Standardization (3) Logistic regression Add polynomial feature Mu ...

preface In the previous blog post, we learned Dimension reduction algorithm PCA , and Parameters of PCA . This article is based on the premise of having a certain foundation for PCA. This paper mainly introduces the application of PCA algorithm in practice. Including the selection of PCA parameters, the use of PCA in training set and test ...

preface Follow the previous section Point cloud 1 Data address of this section: link: https://pan.baidu.com/s/1O4s8tFOvExhuKMl2OCv4Kg Extraction code: 82u1 1. Point cloud clipping Code first import open3d as o3d pcd=o3d.io.read_point_cloud("./test_data/Crop/fragment.ply") val=o3d.visualization.read_selection_polygon_volume("./test_data ...

preface In the supervised learning algorithm of machine learning, our goal is to learn a stable model with good performance in all aspects, but the actual situation is often not so ideal. Sometimes we can only get multiple preferred models (weak supervised model, which performs better in some aspects). Three cobblers make Zhuge Liang. Ensemble ...

Content introduction Abnormal data are often encountered in daily data analysis, that is, data beyond a specific area or range is usually defined as abnormal or "noise", also known as outliers. This paper introduces the visualization and processing routine of data outliers in daily work. Distinguish true and false exceptions J ...

When it comes to anomaly detection, the main challenge is that it is difficult to define anomalies. For example, how can we define and predict abnormal network intrusion, system defects and security fraud? If known exception data has been marked, we can choose from the various types of supervised machine learning models (see the link below fo ...

1. Experimental purpose This notebook is used for the following purposes: Training a single model on multiple time seriesThe pre training model is used to obtain the prediction of any time series not seen during trainingTraining and using models using covariates 2. Guide library # fix python path if working locally from utils import fix_pyt ...

Probabilistic representation of CBOW model: P(A): probability of occurrence of A. P(A,B): the probability of simultaneous occurrence of event A and event B, which is called joint probability. P(A|B): the probability of event A after giving the information of event B, which is called A posteriori probability. CBOW model: the probability of o ...

I Overview of transfer learningTransfer learning refers to applying a network that completes learning tasks in a certain field to a new field for learning tasks.Implementation of transfer learning based on convolution networkFor the trained network model, the first few layers of the network model usually learn general features. With the deepeni ...

preface At present, with the popularity of artificial intelligence, it has attracted the attention of many industries to artificial intelligence. At the same time, it has also ushered in waves of artificial intelligence learning upsurge. Although the principles behind artificial intelligence can not be introduced in detail in a short artic ...