Getting Started with scikit-learn Pipelines for Machine Learning

Why Use Pipelines?

The typical overall machine learning workflow with scikit-learn looks something like this:

1. Load all data into X and y
2. Use X and y to perform a train-test split, creating X_train, X_test, y_train, and y_test
3. Fit preprocessors such as StandardScaler and SimpleImputer on X_train
4. Transform X_train using the fitted preprocessors, and perform any other preprocessing steps (such as dropping columns)
5. Create various models, tune hyperparameters, and pick a final model that is fit on the preprocessed X_train as well as y_train
6. Transform X_test using the fitted preprocessors, and perform any other preprocessing steps (such as dropping columns)
7. Evaluate the final model on the preprocessed X_test as well as y_test

Here is an example code snippet that follows these steps, using an antelope dataset (“antelope.csv”) from a statistics textbook. The goal is to predict the number of spring fawns based on the adult antelope population, annual precipitation, and winter severity. This is a very tiny dataset and should only be used for example purposes! This example skips any hyperparameter tuning, and simply fits a vanilla linear regression model on the preprocessed training data before evaluating it on the preprocessed testing data.

# Step 0: import relevant packages
	import pandas as pd

	from sklearn.model_selection import train_test_split
	from sklearn.preprocessing import OneHotEncoder
	from sklearn.linear_model import LinearRegression

	# Step 1: load all data into X and y
	antelope_df = pd.read_csv("antelope.csv")
	X = antelope_df.drop("spring_fawn_count", axis=1)
	y = antelope_df["spring_fawn_count"]

	# Step 2: train-test split
	X_train, X_test, y_train, y_test = train_test_split(
	    X, y, random_state=42, test_size=3)

	# Step 3: fit preprocessor
	ohe = OneHotEncoder(sparse=False, handle_unknown="ignore")
	ohe.fit(X_train[["winter_severity_index"]])

	# Step 4: transform X_train with fitted preprocessor(s), and perform
	# custom preprocessing step(s)

	train_winter_array = ohe.transform(X_train[["winter_severity_index"]])
	train_winter_df = pd.DataFrame(train_winter_array, index=X_train.index)
	X_train = pd.concat([train_winter_df, X_train], axis=1)
	X_train.drop("winter_severity_index", axis=1, inplace=True)

	# for the sake of example, this "feature engineering" encodes a numeric column
	# as a binary column also ("low" meaning "less than 12" here)
	X_train["low_precipitation"] = [int(x < 12) for x in X_train["annual_precipitation"]]

	# Step 5: create a model (skipping cross-validation and hyperparameter tuning
	# for the moment) and fit on preprocessed training data
	model = LinearRegression()
	model.fit(X_train, y_train)

	# Step 6: transform X_test with fitted preprocessor(s), and perform
	# custom preprocessing step(s)

	test_winter_array = ohe.transform(X_test[["winter_severity_index"]])
	test_winter_df = pd.DataFrame(test_winter_array, index=X_test.index)
	X_test = pd.concat([test_winter_df, X_test], axis=1)
	X_test.drop("winter_severity_index", axis=1, inplace=True)

	X_test["low_precipitation"] = [int(x < 12) for x in X_test["annual_precipitation"]]

	# Step 7: evaluate model on preprocessed testing data
	print("Final model score:", model.score(X_test, y_test))
view raw
ml_example_without_pipelines.py hosted with ❤ by GitHub

An example without pipelines

The train-test split is one of the most important components of a machine learning workflow. It helps a data scientist understand model performance, particularly in terms of overfitting. A proper train-test split means that we have to perform the preprocessing steps on the training data and testing data separately, so there is no “leakage” of information from the testing set into the training set.

#scikit-learn #data-preparation #machine-learning #data-pipeline #data-science #data analysis

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5 Latest Technology Trends of Machine Learning for 2021

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Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

• revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
• microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
• Anaconda 4.2 – Anaconda is an opensource Python package

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Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

• Identifying Diseases and Diagnosis
• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

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Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

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Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

• Improved Unsupervised Algorithms
• Increased Adoption of Quantum Computing
• Enhanced Personalization
• Improved Cognitive Services
• Rise of Robots

**Conclusion
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Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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How To Get Started With Machine Learning With The Right Mindset

You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.

Before we dive into the machine learning world, you should take a step back and think, what is stopping you from getting started? If you think about it, most of the time, we presuppose things about ourselves and assume that to be true without question.

The most normal presumption that we make about ourselves is that we need to have prior knowledge before getting started. Get a degree, complete a course, or have a good understanding of a particular subject.

The truth is that most of the time, this is a lie, the prior knowledge you think you need is most of the time not required or is so big that even experts from the field don’t fully understand it. The Seek of this prior knowledge is a trap that will make you run in circles, which leads us to the next presumption.

The perfect condition, you can’t wait for the ideal environment or situation to get started, things will never be 100% ready, try and fail, then try again. It takes a lot of time to get good at machine learning; you won’t learn all at once and especially at the beginning.

Instead of trying to acknowledge everything before getting started, do a little bit every day; you can make significant progress by creating small things every day for a considerable amount of time. The perfect condition will never exist, do it in your path, be consistent with it, and the results will come.

After you start making little progress every day, you probably will end up having a struggle with something or failing to achieve your goal at a certain point. This feeling is tough; it’s hard to see yourself not making any progress, not having any sense of gratification, and then still not give up.

Machine learning is hard, it might take you a few weeks, months or even years to see progress in a certain point but isn’t any harder than any other technical skill, it requires repetition and dedication to get where you want, you need to test it, make a mistake and learn from i

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