Simple and crude understanding and implementation of machine learning linear regression (two): Preliminary using linear regression api, Case

Linear Regression

learning target

• Ownership in the implementation process of linear regression
• Application LinearRegression or SGDRegressor achieve regression prediction
• We know the assessment criteria and formulas regression algorithm
• Overfitting know the causes and solutions underfitting
• We know principle ridge regression and linear regression differences
• Application Ridge achieve regression prediction
• Application joblib achieve saving and loading models

2.2 linear regression api initial use

1 linear regression API

• sklearn.linear_model.LinearRegression()
  • LinearRegression.coef_: regression coefficient

2 Examples

2.1 Analysis of step

• 1. Obtain data set
• 2. Basic data processing (omitted in this case)
• 3. The feature works (in this case omitted)
• 4. Machine Learning
• The model assessment (omitted in this case)

2.2 Code Process

• Import module

from sklearn.linear_model import LinearRegression

• Construct a data set

x = [[80, 86],
[82, 80],
[85, 78],
[90, 90],
[86, 82],
[82, 90],
[78, 80],
[92, 94]]
y = [84.2, 80.6, 80.1, 90, 83.2, 87.6, 79.4, 93.4]

• Machine learning - training model

# 实例化API
estimator = LinearRegression()
# 使用fit方法进行训练
estimator.fit(x,y)

estimator.coef_

estimator.predict([[100, 80]])