## Study notes Day 46 (making score cards by logistic regression)

api import:
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression as LR
Data acquisition:
df = pd.read_csv('./Data/rankingcard.csv',index_col=0)
Remove duplicates:
When you manually enter or enter more than one line, duplicate data is likely to appear. You need to remove the duplicate values first,
# ...

Posted by **vig0** on *Mon, 21 Feb 2022 13:01:06 +0100*

## [Theory + case practice] logistic regression of Python data analysis

Introduction: logistic regression is a very common means of classification. It belongs to probabilistic nonlinear regression, which is divided into two classification and multi classification regression models. For binary logistic regression, the dependent variable y has only two values of "yes" and "no", which are recorded ...

Posted by **callmubashar** on *Wed, 02 Feb 2022 14:19:05 +0100*

## NLP text classification practical introduction super detailed tutorial

catalogue
preface
1, Data loading
1. Load package
2. Read data
II. Text processing
1. Remove useless characters
2. Text segmentation
3. Remove stop words
4. Remove low-frequency words
5. Divide training set and test set
3, Convert text into vector form
1. Convert text into TF IDF vector
2. Convert text into word2vec vector
3. Conv ...

Posted by **adamwhiles** on *Fri, 28 Jan 2022 02:58:21 +0100*

## The second course of artificial intelligence - logical regression

catalogue
1, Manual derivation of logistic regression gradient descent implementation
2, Classification of iris by logistic regression
Introduction to iris dataset
Introduction to linear classifier
Main steps of designing linear classifier
1. Collect a group of samples with category marks X={x1,x2,..., xN}
2. Determine a criterion functi ...

Posted by **Renegade85** on *Thu, 20 Jan 2022 21:20:42 +0100*

## machine learning in action machine learning algorithm learning notes logistic regression

Logistic Regression
Advantages: low computational cost, easy to understand and implement.
Disadvantages: it is easy to under fit, and the classification accuracy may not be high.
Applicable data types: numerical data and nominal data.
Main idea: according to the existing data, the classification boundary resume regression formula is used to ...

Posted by **cuboidgraphix** on *Tue, 11 Jan 2022 16:17:01 +0100*

## Generation from knowledge map to text -- twelve

2021SC@SDUSC
We continue to analyze the remaining key code of the evaluate() function.
final_scores = self.score(ref, hypo)
Final here_ Scores saves the final evaluation score according to the three evaluation methods mentioned in the previous blog. Here, the score function is called, which is the class function of Evaluate. The code is as ...

Posted by **carichod** on *Wed, 29 Dec 2021 12:56:57 +0100*

## Datawhale - (scikit learn tutorial) - task01 (linear regression and logistic regression) - 202112

Datawhale - (scikit learn tutorial) - task01 (linear regression and logistic regression) - 202112
Link to scikit learn tutorial for DataWhale 1, Linear regression 1. Basic form of linear regression
2. Gradient descent training Assuming a given model
h
(
θ
...

Posted by **joma** on *Wed, 15 Dec 2021 23:24:20 +0100*

## Learning notes of linear regression and logistic regression

Learning source code
import numpy as np
import matplotlib.pyplot as plt
def true_fun(X): # This is the real function we set, that is, the model of ground truth
return 1.5*X + 0.2
np.random.seed(0) # Set random seed
n_samples = 30 # Sets the number of sampling data points
'''Generate random data as training set, and add some noise'''
X_ ...

Posted by **andrewgarn** on *Wed, 15 Dec 2021 22:24:05 +0100*

## Task01 Logical Regression-Linear Regression

1.Logistic Regression
Given Data
X
=
x
1
,
x
2
,
...

Posted by **Schneider707** on *Wed, 15 Dec 2021 20:59:29 +0100*

## How to solve the loss function of logic function and code implementation?

The loss function used in this paper is the loss function constructed by KL discretization, without formula derivation; The code part is a user-defined function, not sklearn.
The loss function constructed by discrete logistic regression KL is:
Where m is the number of samples; p_1 indicates the probability that the label is 1; y^{ ...

Posted by **Quest** on *Tue, 09 Nov 2021 19:55:55 +0100*