(testing signal)

Tag: scikit

Scikit-Learn’s Generalized Linear Models

Or how to make sure the airplane’s altitude is not negative.

Using the Model Builder and AutoML for Creating Lead Decision and Lead Scoring Model in Microsoft…

Step-by-step guide for creating, training, evaluating and consuming machine learning models powered by ML.NETPhoto by Rodolfo Clix from Pexels

Integrating Scikit-learn Machine Learning models into the Microsoft .NET ecosystem using Open Neural Network Exchange (ONNX) format | by Miodrag Cekikj | Sep, 2021

Using the ONNX format for deploying trained Scikit-learn Lead Scoring predictive model into the .NET ecosystem

Photo by Miguel Á. Padriñán from Pexels
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Wavelet Transforms in Python with Google JAX

A simple data compression example

Wavelet transforms are one of the key tools for signal analysis. They are extensively used in science and engineering. Some of the specific applications include data compression, gait analysis, signal/image de-noising, digital communications, etc. This article focuses on a simple lossy data compression application by using the DWT (Discrete Wavelet Transform) support provided in the CR-Sparse library.

For a good introduction to wavelet transforms, please see:

Wavelets in Python

There are several packages in Python which have support for wavelet transforms. Let me list a few:

  • PyWavelets is one of the most comprehensive implementations for wavelet support in python for both discrete and continuous wavelets.
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How to train an Out-of-Memory Data with Scikit-learn

Essential guide to incremental learning using the partial_fit API

Image by PublicDomainPictures from Pixabay

Scikit-learn is a popular Python package among the data science community, as it offers the implementation of various classification, regression, and clustering algorithms. One can train a classification or regression machine learning model in few lines of Python code using the scikit-learn package.

Pandas is another popular Python library that offers to handle and preprocessing data prior to feeding it to a scikit-learn model. One can easily process and train an in-memory dataset (data that can fit into the RAM memory) using Pandas and Scikit-learn packages, but when it comes to working with a large dataset or out-of-memory dataset (data that cannot fit into the RAM memory), it fails, and cause memory issue.

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Algorithmic Thinking for Data Science

The one prominent question that data science students constantly ask is, “Why Algorithms?” And with all honesty, I do not blame them. You see libraries and languages advancing every day, Python with scikit-learn can implement almost any data structure in one line of code. Why would one want to know the science and mathematics behind those inbuilt algorithms then?

It has to do with how “Learning” works, in Humans.

Go back in time to when you started crawling and walking, maybe at the age of 8 months. A few months later, you started hearing and speaking words. In a few years, you could speak with your parents in their taught language and you were able to build conversations with other people in the same language.

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What Is The Difference Between predict() and predict_proba() in scikit-learn?

The predict_proba() method

In the context of classification tasks, some sklearn estimators also implement the predict_proba method that returns the class probabilities for each data point.

The method accepts a single argument that corresponds to the data over which the probabilities will be computed and returns an array of lists containing the class probabilities for the input data points.

predictions = knn.predict_proba(iris_X_test)print(predictions)
array([[0. , 1. , 0. ],
[0. , 0.4, 0.6],
[0. , 1. , 0. ],
[1. , 0. , 0. ],
[1. , 0. , 0. ],
[1. , 0. , 0. ],
[0. , 0. , 1.
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Feature Selection with Genetic Algorithms

A genetic algorithm is a technique for optimization problems based on natural selection. In this post, I show how to use genetic algorithms for feature selection.

While there are many well-known feature selections methods in scikit-learn, feature selection goes well beyond what is available there.

Feature selection is a crucial aspect of any machine learning pipeline. However, these days there is a surplus of available data. As a consequence, there is often a surplus of features.

As is often the case with many features, many are redundant. They add noise to your model and make model interpretation problematic.

The problem is determining what features are relevant to the problem.

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A Data Scientist's Guide to Semi-Supervised Learning

Semi-supervised learning is the type of machine learning that is not commonly talked about by data science and machine learning practitioners but still has a very important role to play. In this type of learning, you will have a small amount of labeled data and a large amount of unlabeled data when training a model that makes a prediction. The latest version of scikit-learn (0.24) has introduced a new self-training implementation for Semi-Supervised learning called SelfTrainingclassifier. SelfTrainingClassifier can be used with any supervised classifier that can return probability estimates.

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Davis David Hacker Noon profile picture

@davisdavidDavis David

Data Scientist | AI Practitioner | Software Developer.

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A Practical Introduction to Grid Search, Random Search, and Bayes Search

For demonstration, we’ll be using the built-in breast cancer data from Scikit Learn to train a Support Vector Classifier (SVC). We can get the data with the load_breast_cancer function:

from sklearn.datasets import load_breast_cancercancer = load_breast_cancer()

Next, let’s create df_X and df_y for features and target label as follows:

# Features
df_X = pd.DataFrame(cancer['data'], columns=cancer['feature_names'])
# Target label
df_y = pd.DataFrame(cancer['target'], columns=['Cancer'])

P.S. If you want to know more about the dataset, you can run print(cancer['DESCR']) to print out summary and feature information.

After that, let’s split the dataset into a training set (70%) and a test set (30%) using training_test_split():

# Train test split
from sklearn.model_selection
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How to Implement Artificial Intelligence Using scikit-learn

This introduction to power Python tools will have you applying AI in no time

Image credit: Ahmed Gad on Pixabay

Data scientists use artificial intelligence (AI) for a vast array of powerful uses. It’s now running control systems reducing building energy consumption, it provides recommendations for clothes to buy or shows to watch, it helps improve farming practices and the amount of food we can grow, and some day it may even be driving our cars for us. Knowing how to use these tools will empower you to solve the next generation of society’s technical challenges.

Fortunately, getting started with artificial intelligence isn’t all that challenging for people who are already experienced with Python and data analysis.

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Build Your First Machine Learning Model With Python in 7 minutes

Using Pandas, NumPy, and Scikit-learn

Photo by Marcel Eberle on Unsplash

When I first started to learn about data science, machine learning sounded like an extremely difficult subject. I was reading about algorithms with fancy names such as support vector machine, gradient boosted decision trees, logistic regression, and so on.

It did not take me long to realize that all those algorithms are essentially capturing the relationships among variables or the underlying structure within the data.

Some of the relationships are crystal clear. For instance, we all know that, everything else being equal, the price of a car decreases as it gets older (excluding the classics).

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How to Master Scikit-learn for Data Science

5.1. Core steps for building and evaluating models

In a nutshell, if I can summarize the core essence of using learning algorithms in scikit-learn it would consist of the following 5 steps:

from sklearn.modulename import EstimatorName      # 0. Import
model = EstimatorName() # 1. Instantiate
model.fit(X_train, y_train) # 2. Fit
model.predict(X_test) # 3. Predict
model.score(X_test, y_test) # 4. Score

Translating the above pseudo-code to the construction of an actual model (e.g. classification model) by using the random forest algorithm as an example would yield the following code block:

from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(max_features=5, n_estimators=100)
rf.fit(X_train, y_train)
rf.predict(X_test)
rf.score(X_test,
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Accelerate your Hyperparameter optimization with scikit-optimize

Get the right balance between underfitting and overfitting by finding optimal hyperparameters for any model

photo by Author

Despite being one of the last stages of creating a model, Hyperparameter optimization (“HPO”) can make all the difference between a good model, which generalizes well, or ugly overfitting, which performs great with the training data but is much worse on the validation set.

This is especially the case with popular Tree Base models such as Random Forest, XGBoost, or CatBoost. Usually, the base model will badly overfit your data. On the other hand, trying to manually increase the bias by setting some hyperparameters like “max_depth” or “max_features” in RandomForest often causes significant underfitting.

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