- Logisticregression python jupyter notebook how to#
- Logisticregression python jupyter notebook install#
- Logisticregression python jupyter notebook code#
Logisticregression python jupyter notebook code#
For example, type the following code in Jupyter notebook and run the code by pressing "Shift + Enter". Now, after verifying the installation, you are ready to write your Tkinter application code in Jupyter notebook. Once we have installed Tkinter in Jupyter notebook, then we can verify the installation by typing the following command − from tkinter import * We can run all the standard commands of Tkinter in Jupyter notebook.
Logisticregression python jupyter notebook install#
Tkinter can be installed on Jupyter notebook as well, by using the command pip install tkinter.
![logisticregression python jupyter notebook logisticregression python jupyter notebook](https://scipython.com/static/media/uploads/blog/logistic_regression/decision-boundary.png)
It will install all the other modules that come with Tkinter library. In Windows operating system, we can install the Tkinter library using the command pip install tkinter. It is completely open-source which works on Windows, Mac, Linux, and Ubuntu. Thanks for reading.Tkinter is a Python library used for creating and developing GUI-based applications. # False negative: 0(lower-left) – Number of negatives we predicted wrongly # False positive: 1 (top-right) – Number of positives we predicted wrongly # True negative: 11(lower-right) – Number of negatives we predicted correctly # True positive: 13 (upper-left) – Number of positives we predicted correctly We can deduce from the confusion matrix that: To see the confusion matrix, use: # Show the Confusion Matrix It tells you the number of True positives, true negatives, false positives and false negatives. The confusion matrix helps you to see how the model performed. (you can view the predicted values using print(y_pred) # Perform prediction using the test dataset We now use the model to predict the outputs given the test dataset. You can view the logistic regression coefficient and intercept using the code below: # Show to Coeficient and Intercept print(lr. Penalty='l2', random_state=None, solver='liblinear', tol=0.0001, Intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1, The logistic regression output is given below: LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, (kind of similar to Linear Regression) # Create a Logistic Regression Object, perform Logistic Regression
![logisticregression python jupyter notebook logisticregression python jupyter notebook](https://regenerativetoday.com/wp-content/uploads/2020/08/hd-with-ag.png)
Here we would create a LogistiRegression object and fit it with out dataset. X_train, x_test, y_train, y_test = train_test_split(x, y, random_state = 1) # Split the dataset into training and test dataset The training dataset is used to train the model while the test dataset is used to test the model’s performance on new data. Now we would split the dataset into training dataset and test dataset. title( 'Scatter Plot of Logistic Regression') Now we would create a simple scatter plot just to see how the data looks like. The code for the make_classification is given below: # Generate and dataset for Logistic Regression
![logisticregression python jupyter notebook logisticregression python jupyter notebook](https://vncoder.vn/upload/img/lesson/1607773608.png)
You need to specify the number of samples, the number of feature, number of classes and other parameters. Now you need to generate the dataset using the make_classification() function. The complete import statement is given below: from sklearn.datasets import make_classificationįrom sklearn.linear_model import LogisticRegressionįrom sklearn.model_selection import train_test_splitįrom trics import confusion_matrix Train_test_split: imported from sklearn.model_selection and used to split dataset into training and test datasetsĬonfusion matrix: imported from trics and used to generate the confusion matrix of the classifiers LogisticRegression: this is imported from sklearn.linear_model.
![logisticregression python jupyter notebook logisticregression python jupyter notebook](https://www.bogotobogo.com/python/scikit-learn/images/scikit-logistic-regression/scikit-logistic-regression.png)
Make_classification: available in sklearn.datasets and used to generate dataset
Logisticregression python jupyter notebook how to#
In this short lesson, I will show you how to perform Logistic Regression in Python.