Scikit-Learn, often abbreviated as sklearn, is a popular machine learning library for Python. Problem Formulation: Given a PyCharm project. How to install the Scikit-Learn library in your project within a virtual environment or globally? Here’s a solution that always works: Open File > Settings > Project from the PyCharm menu. Select your current project. Click …
Logistic regression is a popular algorithm for classification problems (despite its name indicating that it is a âregressionâ algorithm). It belongs to one of the most important algorithms in the machine learning space. Linear Regression Background Letâs review linear regression. Given the training data, we compute a line that fits this training data so that …
Does your model’s prediction accuracy suck but you need to meet the deadline at all costs? Try the quick and dirty âmeta-learningâ approach called ensemble learning. In this article, you’ll learn about a specific ensemble learning technique called random forests that combines the predictions (or classifications) of multiple machine learning algorithms. In many cases, it …
Support Vector Machines (SVM) have gained huge popularity in recent years. The reason is their robust classification performance â even in high-dimensional spaces: SVMs even work if there are more dimensions (features) than data items. This is unusual for classification algorithms because of the curse of dimensionality â with increasing dimensionality, data becomes extremely sparse …
SVM sklearn: Python Support Vector Machines Made Simple Read More »
Decision Trees are powerful and intuitive tools in your machine learning toolbelt. Decision trees are human-readable â in contrast to most other machine learning techniques. You can easily train a decision tree and show it to your supervisors who do not need to know anything about machine learning in order to understand how your model …
Python Scikit-Learn Decision Tree [Video + Blog] Read More »
Neural Networks have gained massive popularity in the last years. This is not only a result of the improved algorithms and learning techniques in the field but also of the accelerated hardware performance and the rise of General Processing GPU (GPGPU) technology. In this article, youâll learn about the Multi-Layer Perceptron (MLP) which is one …
Summary: Use SKLearnâs LogisticRegression Model for classification problems only. The Y variable is a category (e.g., binary [0,1]), not continuous (e.g. float numbers 3.4, 7.9). If the Y variable is non-categorical (i.e., continuous), the potential fixes are as follows. Re-examine the data. Try to encode the continuous Y variable into categories (e.g., use SKLearnâs LabelEncoder preprocessor). Re-examine …
[Fixed] Unknown label type: âcontinuous’ in sklearn LogisticRegression Read More »
Scikit-learn has a library of transformers to preprocess a data set. These transformers clean, generate, reduce or expand the feature representation of the data set. These transformers provide the fit(), transform() and fit_transform() methods. The fit() method identifies and learns the model parameters from a training data set. For example, standard deviation and mean for …
Sklearn fit() vs transform() vs fit_transform() – What’s the Difference? Read More »
Summary: The best way to plot a Confusion Matrix with labels, is to use the ConfusionMatrixDisplay object from the sklearn.metrics module. Another simple and elegant way is to use the seaborn.heatmap() function. Note: All the solutions provided below have been verified using Python 3.9.0b5. Problem Formulation Imagine the following lists of Actual and Predicted values …
How To Plot SKLearn Confusion Matrix With Labels? Read More »