Tag:Spam

  • Design of machine learning system

    Time:2022-3-22

    Today, little Mi will take you to learn how to design a machine learning system and some problems you may encounter when designing a complex machine learning system. Of course, in addition, Xiaomi will also provide some small tips on skillfully building complex machine learning systems. Oh, by the way, secretly telling you may help […]

  • “Mindspire: machine learning with little Mi!” Introduction

    Time:2022-2-24

    Recently, little Mi found a problem! Why is my style recommended by a song listening software? Why does an orange shopping software recommend many to me, so that I can plant grass directly? Little Mi had an idea. He took out his mobile phone and began to ask Baidu for help. After a search, he […]

  • Key terms of machine learning

    Time:2021-11-3

    Series articles: Opencv black hat operation Key terms of machine learning Main terms Label: label is what we want to predict, that is, the Y variable in simple linear regressionCharacteristics: characteristics are input variables, i.e. x variables in simple linear regression. Simple machine learning projects may use a single feature, while more complex machine learning […]

  • Classification of machine learning: specifying thresholds

    Time:2021-10-21

    Logistic regression returns probability. You can use the returned probability “as is” (for example, the probability of users clicking on this advertisement is 0.00023), or you can convert the returned probability into a binary value (for example, this email is spam).If a logistic regression model predicts an e-mail with a return probability of 0.9995, it […]

  • Classification of machine learning: accuracy and recall

    Time:2021-10-4

    Accuracy rate Accuracy rateThe indicator tries to answer the following questions:What is the proportion of samples identified as positive categories?Accuracy is defined as follows: Precision = \dfrac{TP}{TP + FP} Note: if there are no false positive examples in the prediction results of the model, the accuracy rate of the model is 1.0.Let’s calculatePrevious partAccuracy of […]

  • Classification of machine learning: prediction bias

    Time:2021-9-29

    The logistic regression prediction should be unbiased. That is, the “predicted average” should be approximately equal to the “observed average”Prediction deviationIt refers to the difference between the two averages. Namely:Forecast deviation = forecast average – the average of the corresponding labels in the datasetNote: “forecast deviation” is not the same as “Deviation” (“B” in “Wx […]

  • Multi class neural networks for machine learning: softmax

    Time:2021-9-16

    We already know,logistic regression Decimals between 0 and 1.0 can be generated. For example, the logistic regression output value of an e-mail classifier is 0.8, indicating that the probability of e-mail being spam is 80%, and the probability of not being spam is 20%. Obviously, the sum of the probability that an e-mail is spam […]

  • 1 Introduction

    Time:2021-8-1

           Statistical learningIt plays a key role in many areas of science, finance and industry. Here are some examples of learning problems:        ■   Predict whether patients hospitalized for a heart attack will have another heart attack. The prediction will be based on the patient’s demographic, dietary and clinical measurements.        ■   Predict the stock price in the […]

  • Introduction to NLP (1): n-gram language model.

    Time:2021-2-14

    The article comes from the official account: machine learning alchemy. N-gram language model N-gram is a language model, which is a probability model. The input of this model is a sentence, and the output is a probability. If given in I love deep learningl love ( ) learningThen the probability of filling deep in the […]

  • Machine learning (12): Wu Enda’s notes

    Time:2021-1-12

    This article mainly looks at several problems that should be considered in machine learning. We take spam classification as an example. Given some training sets with tags, spam y = 1, non spam y = 0, we construct a classifier by supervised learning. ###First, consider how to construct the vector X In spam classification, we […]

  • NLP Introduction (1) n-gram language model.

    Time:2020-9-3

    The article comes from the official account: machine learning alchemy. N-gram language model N-gram is a language model and a probability model. The input of this model is a sentence and the output is a probability. I love deep learningl love ( ) learningThe probability of filling in deep in the air is higher than […]

  • Machine learning based on C ා spam filtering

    Time:2020-7-31

    In this chapter, we will build a spam filtering classification model. We will use a raw email dataset that contains both spam and non spam and use it to train our ML model. We will begin to follow the steps for developing ml models discussed in the previous chapter. This will help us understand the […]