Machine LearningPhoto by Isaac Smith on UnsplashModel evaluation and validation are important parts of supervised machine learning. It aids in the selection of the best model to represent our data as well as the prediction of how well that model will perform in the future.To predict this model we need to split this model dataset into training and testing data. Manually splitting out this data is difficult because of the large size of datasets and data needs to be shuffled.For making this task easier we will use Scikit-learn’s train_test_split() module, which will split our data into…
Python and Google Colab ProjectContinue reading on Towards AI »
Scikit-learn is the most popular open-source and free python machine learning library for Data scientists and Machine learning practitioners. The scikit-learn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.Read the full story
Lets review a classical model from Pierian Data. Your neighbor is a real estate agent and wants some help predicting housing prices for regions in the USA. It would be great if you could somehow create a model with Python and scikit-learn for her, that allows her to put in a few features of a house and returns back an estimate of what the house would sell for.
She has asked you if you could help her out with your new data science skills. You say yes, and decide that Linear Regression might be a good path to solve this problem!.
Your neighbor then gives you some information about a bunch of houses in regions of the United States,it is all in the data set: USA_Housing.csv.
The data contains the following columns:
‘Avg. Area Income’: Avg.… Read more...
A short tutorial on how to split a dataset into training and test sets through scikit-learn, pandas, or NumPy builtin functionsContinue reading on Towards AI »
Luckily, the lazy habit of writing “bug fixes and stability improvements” hasn’t found its way to the software libraries’ release notes . Without checking these notes, I wouldn’t have realized that Scikit-Lean version 0.23 implements Generalized Linear Models (GLM).
I pay extra attention to Scikit-Learn. Not only because I use it all the time, but also, after publishing my book, Hands-On Machine Learning with Scikit-learn and Scientific Python Toolkits, I want to keep track of the library’s newly implemented algorithms and features to write about them here as a pseudo-appendix to my book.
As its name suggests, the Generalized Linear Model is an extension to our ultimate favorite Linear Regression algorithm.… Read more...
What do we want to achieve?
We want to train an AI agent or model that can do something like this,
Image source: Prepared by the author using this Pixabay image (Free to use)
Variances, anomalies, shifts
Little more specifically, we want to train an AI agent (or model) to identify/classify time-series data for,
anomaly frequencies (little or high fraction of anomalies)
anomaly scales (are the anomalies too far from the normal or close)
a positive or negative shift in the time-series data (in the presence of some anomalies)
But, we don’t want to complicate things
However, we don’t want to do a ton of feature engineering or learn complicated time-series algorithms (e.g. ARIMA) and properties (e.g.… Read more...
Recently, I wrote an article explaining the utilization of the ONNX format in integrating the Scikit-learn lead scoring machine learning model into the .NET ecosystem. I described one possible way of deploying the Python-based regression model as Microsoft Azure Function. That is a procedure applicable for integrating the trained model as part of the Web API or Console Application as well. What I have mentioned there was the opportunity to use the approach for bridging the technical differences between the different data science and application development platforms, in this case targeting the .NET… Read more...
Using the ONNX format for deploying trained Scikit-learn Lead Scoring predictive model into the .NET ecosystem
While being part of a team working on designing and developing a lead scoring system prototype, I faced the challenge of integrating machine learning models into the target environment built around the Microsoft .NET ecosystem. Technically, I implemented the lead scoring predictive model using the Scikit-learn machine learning built-in algorithm for regression, more precisely Logistic Regression. Considering the phases of initial data analysis, data preprocessing, exploratory data analysis (EDA), and the data preparation for the model building itself, I used the Jupyter Notebook environment powered by Anaconda distribution for Python scientific computing.
Linear Regression is usually the first algorithm that people learn for Machine Learning and Data Science. Linear Regression is a linear model that assumes a linear relationship between the input variables (
X) and the single output variable (
y). In general, there are two cases:
- Single Variable Linear Regression: it models the relationship between a single input variable (single feature variable) and a single output variable.
- Multi-Variable Linear Regression (also known as Multivariate Linear Regression): it models the relationship between multiple input variables (multiple features variables) and a single output variable.
This algorithm is common enough that Scikit-learn has this functionality built-in with
By David B Rosen (PhD), Lead Data Scientist for Automated Credit Approval at IBM Global Financing
In machine learning, when building a classification model with data having far more instances of one class than another, the initial default classifier is often unsatisfactory because it classifies almost every case as the majority class. Many articles show you how you could use oversampling (e.g. SMOTE) or sometimes undersampling or simply class-based sample weighting to retrain the model on “rebalanced” data, but this isn’t always necessary. Here we aim instead to show how much you can do without balancing the data or retraining the model.
We do this by simply adjusting the the threshold for which we say “Class 1” when the model’s predicted probability of Class 1 is above it in two-class classification, rather than naïvely using the default classification rule which chooses which ever class is predicted to be most probable (probability threshold of 0.5).
What is a Deep Learning Framework?
A deep learning framework is a software package used by researchers and data scientists to design and train deep learning models. The idea with these frameworks is to allow people to train their models without digging into the algorithms underlying deep learning, neural networks, and machine learning.
These frameworks offer building blocks for designing, training, and validating models through a high-level programming interface. Widely used deep learning frameworks such as PyTorch, TensorFlow, MXNet, and others can also use GPU-accelerated libraries such as cuDNN and NCCL to deliver high-performance multi-GPU accelerated training.
Why Use a Deep Learning Framework?
- They supply readily available libraries for defining layers, network types (CNNs, RNNs), and common model architectures
- They can support computer vision applications; image, speech, and natural language processing
- They have familiar interfaces via popular programming languages such as Python, C, C++, and Scala
- Many deep learning frameworks are accelerated by NVIDIA deep learning libraries such as cuDNN, NCCl, and cuBLAS for GPU accelerated deep learning training
By Zulie Rane, Freelance Writer and Coding Enthusiast
If you’re thinking about dipping your toe into the finance sector for your career and you stumble across this article, you may be wondering, “How can Python help in finance?”
You, like me, may be surprised to learn that you should learn to code altogether – and even more surprised to learn that the best language for finance is a popular data science language, Python. Learning financial programming with Python is becoming a requirement.
Finance and banking have a reputation for very high salaries, so the job field attracts a large number of applicants. If you’re one of them, you should know Python is hugely popular for finance — and still growing in popularity. Python is widely used in risk management, the creation of trading bots, quantitative finance for analyzing big financial data, and more.
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.
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.
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. This was before you went to school.
You work as a data scientist for an auction company, and your boss asks you to build a model to predict the hammer price (i.e. the final selling price) of the items on sale. Such a model will serve two purposes:
- setting a meaningful opening bid for each item;
- placing the most expensive items at periodic intervals during the auction. In this way, you will keep up the attention of the audience.
Since you want to predict a point value (in $), you decide to use a regression model (for instance,
XGBRegressor()). Now, how do you evaluate the performance of your model?
Let’s see Scikit’s metric toolbox for regression models:
All these metrics seek to quantify how far model predictions are from the actual values.
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. ],
[0. , 1. , 0. ],
[0. , 0. , 1. ],
[1. , 0. , 0. ]])
Continue reading: https://towardsdatascience.com/predict-vs-predict-proba-scikit-learn-bdc45daa5972?source=rss—-7f60cf5620c9—4
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. The aim is to have quality features.
Almost in every machine learning project, we train and evaluate multiple machine learning models. This often involves writing multiple lines of imports, many function calls, print statements to train individual models and compare the results across the models. The code becomes a mess when comparing different models with cross-validation loops or ensembling the models. Over time, it gets even messier when we move from classification models to regression models or vice-versa. We end up copying snippets of code from one place to another, creating chaos! We can easily avoid this chaos by just importing PyCaret!
PyCaret is a low-code machine library that allows you to create, train, and test ML models via a unified API given a regression or classification problem.
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.
Data Scientist | AI Practitioner | Software Developer. Giving talks, teaching, writing.
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.
An understanding of key skill areas to identify talented machine learning engineers. Such an understanding will help in recruiting, allocating, and promoting the engineers.
Machine Learning Engineer (MLE) is one of the hottest roles these days. While many would associate such a role with Python, R, random forest, convolutional neural network, PyTorch, scikit-learn, bias-variance tradeoff, etc., a lot more things come in the path of these engineers. Things that an MLE needs to handle does not only derived from the field of Machine Learning (ML) but also from other technical and soft disciplines. As depicted in Figure 1, in addition to possessing ML skills, an MLE needs to know programming, (big) data management, cloud solutions, and system engineering.
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
from sklearn.datasets import load_breast_cancercancer = load_breast_cancer()
Next, let’s create
df_y for features and target label as follows:
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
# Train test split
In this article, we’ll cover the fundamentals you need to know to use LASSO regression:
- We’ll briefly cover the theory behind LASSO.
- We’ll talk about why correct usage of LASSO requires features with similar scales.
- We’ll cover how to interpret the coefficients in Linear Regression and LASSO Regression with standardized features.
- We’ll introduce the dataset and give some insight into why LASSO helps.
- We’ll show how to implement Linear Regression, LASSO Regression and Ridge Regression in SciKit-Learn.
In a previous article, we discuss how and why LASSO increases the interpretability and accuracy of Generalized Linear Models. We’ll recap the basics here, but if you are interested in a deeper dive into the theory, have a look at the article below.
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. You can leverage the powerful scikit-learn package to do most of the hard work for you.