Programming VIP

Decision Tree Picks Out Good Watermelons 1. ID3 Algorithmic Theory (1) algorithm core The core of the ID3 algorithm is to select the partitioned features based on the information gain and then build the decision tree recursively (2) Feature selection Feature selection means selecting the optimal partition attribute and selecting a feat ...

1, Decision tree 1. Concept Decision tree is a classification algorithm based on tree structure. We hope to learn a model (i.e. decision tree) from a given training data set and use the model to classify new samples. The decision tree can intuitively show the classification process and results. Once the model is built successfully, the cl ...

preface In my last blog, I briefly explained the call of scikit fuzzy, and explained it with a case commonly used by big guys. In recent learning, I found other functions in skfuzzy, including automatic setting and visual display of reference values, which are introduced below. 1, Case description Similarly, it is the classic two inputs, one ...

Written in front: This article is some new thoughts I got after Rereading SSD (SSD: Single Shot MultiBox Detector) and fastercnn (fast r-cnn: directions real time object detection with region proposal networks), and these understandings are deeper for me. Thanks for the basic explanation from Bubbliiiing, University of science and technol ...

About parameter acquisition: it has been proposed in the previous blog, please refer to For relevant links, please click 1, Analysis input and output 1. The handwriting input is a 28x28 black-and-white picture, so the input is 784 X 2. The output is the probability of identifying numbers 0-9, so there are 10 outputs 3. The input can only ...

  catalogue Construction of decision tree General process of decision tree information gain   Write code to calculate empirical entropy Calculate information gain using code Partition dataset Select the best data set division method   Information gain rate Gini coefficient   Differences between ID3, C4.5 and CART I ...

1, Experimental purpose [experiment content] Develop a production system that can identify seven kinds of animals, such as tigers, leopards, zebras, giraffes, penguins, ostriches and albatrosses. [rule base] IF hairy THEN mammal IF milk THEN mammals IF feathered THEN bird IF can fly AND can lay eggs THEN bird IF eating meat THEN carnivores ...

preface It was also a Web topic in WMCTF2021 a long time ago. There was no clue at that time, and then it did not reappear. I learned a lot from president Zhao's blog this evening. This article just followed Zhao's blog for a wave of reproduction and recorded it, that's all. Topic environment The topic itself is given to the shell, but there ...

1, Convolutional neural network CNN   The most classical convolutional neural network has three layers: Convolution LayerSampling on the Pooling Layer (Subsampling)Fully Connected Layer Calculation of convolution: Matrix multiplication with the blue matrix filter in the red box, that is: (2*1+5*0+5*1)+(2*2+3*1+4*3)+(4*1+3*1+1*2)= 35 ...

ID3 Decision Tree Algorithm 1. Theory purity For a branch node, if all the samples it contains belong to the same class, its purity is 1, and we always want the higher the purity, the better, that is, as many samples as possible belong to the same class. So how do you measure "purity"? The concept of "information entropy&q ...