Updated March 15, 2023
Introduction to Scikit Learn Logistic Regression
The following article provides an outline for Scikit Learn Logistic Regression. Logistic regression means classification algorithm instead of regression algorithm. It is usually based on the independent variables and is used to estimate the discrete value. In other words, we can say that it is used to calculate the relationship between the categorical dependent variables and the independent variable; here independent variable may be more used to determine the probability and is also used in logistics function.
Key Takeaways
- It is effortless to implement and interpret with an efficient way to train the data.
- It does not allow us to make assumptions about the classes.
- We can extend multiple classes that we can call multinomial regression by using logistic regression.
- In logistic regression, we cannot predict the values.
Overview of Scikit Learn Logistic Regression
Logistic regression is a regulated grouping algorithm. Logistic regression can be utilized for different characterization issues like spam identification. Diabetes expectation, if a given client will buy a specific item or stir another contender, regardless of whether the client will tap on a given notice, and a lot more models are in the can.
Strategic Regression is one of the most basic and generally utilized Machine Learning calculations for two-class characterization. It is not difficult to carry out and can be used as the standard for any double order issue. Its essential ideas are likewise valuable for profound learning. Logistic regression depicts and appraises the connection between one double ward factor and autonomous factors.
Logistic regression is a factual strategy for foreseeing parallel classes. The result or target variable is dichotomous. Dichotomous means there are just two potential classes. For instance, it tends to be utilized for disease location issues. It figures the likelihood of a rare event. It is an exceptional instance of direct relapse where the objective variable is downright in nature. It involves a log of chances as the reliant variable. Strategic Regression predicts the likelihood of an event on a parallel occasion using a logit capability.
How to Use Logistic Regression in Scikit Learn?
Let’s see how we can use logistic regression in Scikit Learn as follows:
First, we need to import all required libraries and the dataset from the sklearns; here, we will use the split function to split the data into the sample data, as shown below.
Code:
import pandas as pd
import matplotlib.pyplot as pt
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_breast_cancerIn the next step, we need to load the dataset into the local variable.
Code:
dataset = load_wine()In the third step, we need to apply the split function; in this step, we need to split the data into the train data, which means we can use an 80-20 split structure. 80 % is training data, and the remaining is test data.
Now the time is to make a logistic regression model as shown in the below screenshot.
Now we need to train the downloaded data with the help of the below screenshot as follows.
Next, we need to make the prediction of test data, so here the model use the trained parameter from the downloaded data.
Now in the next step we need to evaluate the result, so we need to use the accuracy function as well as predicted labels to find the accuracy of the model as shown in the below screenshot.
Finally, we just print the result.
Now let’s combine all the above codes as below.
Code:
import pandas as pd
import matplotlib.pyplot as pt
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_breast_cancer
data_s=load_breast_cancer()
data_s.keys()
x_datatrain, x_datatest, y_datatrain, y_datatest = train_test_split(data_s.data, data_s.target, test_size=0.20, random_state=15)
l_model = LogisticRegression()
l_model.fit(x_datatrain, y_datatrain)
y_predictiondata = l_model.predict(x_datatest)
acc = accuracy_score(y_datatest,y_predictiondata)*100
con_mat = confusion_matrix(y_datatest,y_predictiondata)
print("Accuracy will be",acc)
print(con_mat)Explanation:
- The end result of the above implementation is shown in the below screenshot.
Output:
Scikit Learn Logistic Regression Model
Let’s see the logistic regression model as follows:
For developing the model, we need to follow different steps as follows:
First, we must import the logistic regression module and create a classifier for logistic regression, as shown in the screenshot below.
After that, we need to make the prediction to fit the data with the model as shown in the below screenshot.
After that, we need to evaluate the result with help of the confusion matrix which we already see in the above example as well as shown below screenshot.
Scikit Learn Logistic Regression Parameters
Let’s see what are the different parameters we require as follows:
- Penalty: With the help of this parameter, we can specify the norm that is L1 or L2.
- Dual: This is a boolean parameter used to formulate the dual but is only applicable for L2 penalty.
- Tol: It is used to show tolerance for the criteria.
- C: It is used to represent the regulation strength and floats.
- fit_intercept: This is used to specify the constant and to add the decision function.
- intercept_scaling: It is used to set fit_intercept as accurate.
- class_weight: It is used to show the weight associated with classes.
- random_state: During the execution, sometimes random numbers are generated at that time; we can use random_state.
- Solver: By using these parameters, we can show which algorithm we used to optimize the problem.
As well as it also provides many parameters, which we can use as per our requirements.
FAQ
Given below are the FAQ mentioned:
Q1. What is logistic regression in Scikit?
Answer:
Basically it is a machine learning algorithm that comes under the classification and it is used to predict the probability of the dependent variable.
Q2. How to use logistic regression in Python?
Answer:
There are several steps we need to follow to implement the logistic regression, so first we need to import the packages, then get data from the dataset and create the model and evaluate the model.
Q3. Why do we need to use logistic regression?
Answer:
Because of easy implementation, interpret as well as it is easy to implement, if incase we have less number of features then we cannot use logistic regression.
Conclusion
In this article, we are trying to explore Scikit Learn logistic regression. In this article, we saw the basic ideas of Scikit Learn logistic regression as well as what are the uses, and features of these Scikit Learn logistic regression. Another point from the article is how we can see the basic implementation of Scikit Learn logistic regression.
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