Python 机器学习

import numpy as np
from sklearn import linear_model
import sklearn.metrics as sm
import matplotlib.pyplot as plt

input_file = r'C:\linear.txt' # 将 'input' 更改为 'input_file' 以避免与内置函数冲突

# 接下来，我们需要加载这些数据。我们使用 np.loadtxt 函数来加载它。
input_data = np.loadtxt(input_file, delimiter=',')
X, y = input_data[:, :-1], input_data[:, -1]

training_samples = int(0.6 * len(X))
# testing_samples = len(X) - num_training # 修正：num_training 应该使用 training_samples
# testing_samples = len(X) - training_samples # 实际上 testing_samples 不需要显式计算，因为可以直接切片

X_train, y_train = X[:training_samples], y[:training_samples]
X_test, y_test = X[training_samples:], y[training_samples:]

reg_linear = linear_model.LinearRegression()

reg_linear.fit(X_train, y_train)

y_test_pred = reg_linear.predict(X_test)

plt.scatter(X_test, y_test, color='red')
plt.plot(X_test, y_test_pred, color='black', linewidth=2)
plt.xticks(())
plt.yticks(())
plt.show()

print("Regressor model performance:")
print("Mean absolute error(MAE) =", round(sm.mean_absolute_error(y_test, y_test_pred), 2))
print("Mean squared error(MSE) =", round(sm.mean_squared_error(y_test, y_test_pred), 2))
print("Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred), 2))
print("Explain variance score =", round(sm.explained_variance_score(y_test, y_test_pred), 2))
print("R2 score =", round(sm.r2_score(y_test, y_test_pred), 2))

Regressor model performance:
Mean absolute error(MAE) = 1.78
Mean squared error(MSE) = 3.89
Median absolute error = 2.01
Explain variance score = -0.09
R2 score = -0.09

15. Regression Algorithms Machine
 – Overview
 Learning with Python
Introduction to Regression
Regression is another important and broadly used statistical and machine learning tool.
The key objective of regression-based tasks is to predict output labels or responses which
are continues numeric values, for the given input data. The output will be based on what
the model has learned in training phase. Basically, regression models use the input data
features (independent variables) and their corresponding continuous numeric output
values (dependent or outcome variables) to learn specific association between inputs and
corresponding outputs.
Y-Output
variables,
dependent on
Input
X-Input variables,
independent in nature
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Types of Regression Models
Machine Learning with Python
Regression Models
Simple
 Multiple
(Univariate Features)
 (Multiple Features)
Regression models are of following two types:
Simple regression model: This is the most basic regression model in which predictions
are formed from a single, univariate feature of the data.
Multiple regression model: As name implies, in this regression model the predictions
are formed from multiple features of the data.
Building a Regressor in Python
Regressor model in Python can be constructed just like we constructed the classifier.
Scikit-learn, a Python library for machine learning can also be used to build a regressor in
Python.
In the following example, we will be building basic regression model that will fit a line to
the data i.e. linear regressor. The necessary steps for building a regressor in Python are
as follows:
Step1: Importing necessary python package
For building a regressor using scikit-learn, we need to import it along with other necessary
packages. We can import the by using following script:
import numpy as np
from sklearn import linear_model
import sklearn.metrics as sm
import matplotlib.pyplot as plt
Step2: Importing dataset
After importing necessary package, we need a dataset to build regression prediction
model. We can import it from sklearn dataset or can use other one as per our requirement.
We are going to use our saved input data. We can import it with the help of following
script:
input = r'C:\linear.txt'
Next, we need to load this data. We are using np.loadtxt function to load it.
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input_data = np.loadtxt(input, delimiter=',')
X, y = input_data[:, :-1], input_data[:, -1]
Machine Learning with Python
Step3: Organizing data into training & testing sets
As we need to test our model on unseen data hence, we will divide our dataset into two
parts: a training set and a test set. The following command will perform it:
training_samples = int(0.6 * len(X))
testing_samples = len(X) - num_training
X_train, y_train = X[:training_samples], y[:training_samples]
X_test, y_test = X[training_samples:], y[training_samples:]
Step4- Model evaluation & prediction
After dividing the data into training and testing we need to build the model. We will be
using LineaRegression() function of Scikit-learn for this purpose. Following command
will create a linear regressor object.
reg_linear= linear_model.LinearRegression()
Next, train this model with the training samples as follows:
reg_linear.fit(X_train, y_train)
Now, at last we need to do the prediction with the testing data.
y_test_pred = reg_linear.predict(X_test)
Step5- Plot & visualization
After prediction, we can plot and visualize it with the help of following script:
plt.scatter(X_test, y_test, color='red')
plt.plot(X_test, y_test_pred, color='black', linewidth=2)
plt.xticks(())
plt.yticks(())
plt.show()
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Output
Machine Learning with Python
In the above output, we can see the regression line between the data points.
Step6- Performance computation: We can also compute the performance of our
regression model with the help of various performance metrics as follows:
print("Regressor model performance:")
print("Mean absolute error(MAE) =", round(sm.mean_absolute_error(y_test,
y_test_pred), 2))
print("Mean squared error(MSE) =", round(sm.mean_squared_error(y_test,
y_test_pred), 2))
print("Median absolute error =", round(sm.median_absolute_error(y_test,
y_test_pred), 2))
print("Explain variance score =", round(sm.explained_variance_score(y_test,
y_test_pred), 2))
print("R2 score =", round(sm.r2_score(y_test, y_test_pred), 2))
Output
Regressor model performance:
Mean absolute error(MAE) = 1.78
Mean squared error(MSE) = 3.89
Median absolute error = 2.01
Explain variance score = -0.09
R2 score = -0.09
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Machine Learning with Python
Types of ML Regression Algorithms
The most useful and popular ML regression algorithm is Linear regression algorithm which
further divided into two types namely:

 Simple Linear Regression algorithm

 Multiple Linear Regression algorithm.
We will discuss about it and implement it in Python in the next chapter.
Applications
The applications of ML regression algorithms are as follows:
Forecasting or Predictive analysis: One of the important uses of regression is
forecasting or predictive analysis. For example, we can forecast GDP, oil prices or in simple
words the quantitative data that changes with the passage of time.
Optimization: We can optimize business processes with the help of regression. For
example, a store manager can create a statistical model to understand the peek time of
coming of customers.
Error correction: In business, taking correct decision is equally important as optimizing
the business process. Regression can help us to take correct decision as well in correcting
the already implemented decision.
Economics: It is the most used tool in economics. We can use regression to predict
supply, demand, consumption, inventory investment etc.
Finance: A financial company is always interested in minimizing the risk portfolio and
want to know the factors that affects the customers. All these can be predicted with the
help of regression model.