Maarit Widmann
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Articles by Maarit Widmann
Understanding and Applying Correspondence Analysis
Customer segments, personality profiles, social classes, and age generations are examples of effective references to larger groups of people sharing similar characteristics. The characteristics that shape these groups are often manifold and thus require multivariate analysis. One way to access the variables is via questionnaires. Because the variables are mostly qualitative, the questionnaires produce categorical data with predefined categories, for example, on a Likert-type scale.
Generate synthetic data to teach machine learning
DATA SCIENCE THEORY | SYNTHETIC DATA | MACHINE LEARNING As an instructor, you'll know just how much exercises contribute to a student's understanding of a topic. But coming up with an instructive and interesting exercise is difficult. The concepts your students are learning vary, they require different kinds of datasets, and the datasets need to be simple enough to allow the student to focus on the task at hand. For our own self-paced machine learning course, we came up with a novel solution.
Understanding and Applying Correspondence Analysis
Key Takeaways Simple correspondence analysis (CA) is a technique to analyze relationships between categorical variables and create profiles based on the projections of the original variables to the new dimensions that it creates CA begins with transformation of the raw data into a two-way contingency table that shows the frequency distribution of the variables Based on the proximity along the first few of the projected dimensions, we can visually explore the individuals’ and categories’...
Seven Techniques for Data Dimensionality Reduction
DATA SCIENCE THEORY | DIMENSIONALITY REDUCTION | KNIME ANALYTICS PLATFORM The recent explosion of data set size, in number of records and attributes, has triggered the development of a number of big data platforms as well as parallel data analytics algorithms. At the same time though, it has pushed for the usage of data dimensionality reduction procedures. Indeed, more is not always better. Large amounts of data might sometimes produce worse performances in data analytics applications.
Will They Blend? Microsoft SharePoint Meets Google Cloud Storage
Read more about author Maarit Widmann. In the “Will They Blend?” blog series, we experiment with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source DevOps on the cloud with protected internal legacy tools, SQL with NoSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: Will they blend?
Visual Scoring Techniques for Classification Models
By Maarit Widmann, Data Scientist, KNIME Is 99% accuracy good for a churn prediction model? If in reality 1% of the customers churn and 99% don’t, the model is doing equally well as a random guess. If 10% of the customers churn and 90% don’t, then the model is doing better than the random guess. Accuracy statistics, such as overall accuracy, quantify the expected performance of a Machine Learning model on new data without any baseline, such as a random guess or existing models.
Seeing the Forest for the Trees – Cohort Analysis - DATAVERSITY
Click to learn more about co-author Maarit Widmann. Click to learn more about co-author Felix Kergl-Räpple. A marketing campaign or publication of a new release can make numbers of customers boom for a while. But what are the effects in the long run? Do the customers stay or churn? Do any of them return at some point? Does the overall revenue increase?
Time Series Analysis with Components
Click to learn more about co-author Maarit Widmann. Click to learn more about co-author Daniele Tonini. Click to learn more about co-author Corey Weisinger. Time Series Forecasting is often neglected says Professor Daniele Tonini: “Considering the plethora of articles, applications, web tutorials and challenges on the data science subject that we’re seeing in the last 3-5 years, it can be pretty surprising to find only a few of them dedicated to time series analysis and forecasting.
Four Techniques for Outlier Detection
DATA SCIENCE THEORY | OUTLIER DETECTION | KNIME ANALYTICS PLATFORM Co-author: Moritz Heine Anomalies, or outliers, can be a serious issue when training machine learning algorithms or applying statistical techniques. They are often the result of errors in measurements or exceptional system conditions and therefore do not describe the common functioning of the underlying system. Indeed, the best practice is to implement an outlier removal phase before proceeding with further analysis.
Outlier Detection Techniques for Fraud
Based on an August 2020 report by Interpol, more people have been spending time online since the start of the coronavirus pandemic, which has resulted in increased cybercrime. UK Finance also claimed remote banking fraud losses soared by 21% to reach 80 million pounds during the first half of 2020. Online fraud is on the rise.
Building a Time Series Analysis Application
Click to learn more about author Maarit Widmann. A complete time series analysis application covers the steps in a Data Science cycle from accessing to transforming, modeling, evaluating, and deploying time series data. However, for time series data the specific tasks in these steps differ in comparison to cross-sectional data.
Will They Blend? Theobald Meets HANA
Click to learn more about author Maarit Widmann. In the “Will They Blend?” blog series, we experiment with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: Will they blend?
Resampling Imbalanced Data and Its Limits
By Maarit Widmann, Data Scientist at KNIME Introduction Car parking ticket machines used to only accept coins. A self-service vegetable stand used to only accept cash. And not such a long time ago I could buy a bus ticket from the bus driver! These days, however, you can (and you’re often encouraged to) pay for these and many other products and services by credit card. This leads to more and more transactions and also to types of transactions that didn’t exist before.
Numeric Scoring Metrics: Find the Right Metric for a Prediction Model
Quantitative data have endless stories to tell! Daily closing prices tell us about the dynamics of the stock market, small smart meters about the energy consumption of households, smartwatches about what’s going on in the human body during an exercise, and surveys about some people’s self-estimation of a topic at some point in time. Different types of experts can tell these stories: financial analysts, data scientists, sports scientists, sociologists, psychologists and so on.
From Modeling to Scoring: Correcting Predicted Class Probabilities in Imbalanced Datasets
Click to learn more about co-author Maarit Widmann. Click to learn more about co-author Alfredo Roccato. This is the second part of a the From Modeling to Scoring Series, see Part One here. Wheeling like a hamster in the Data Science cycle? Don’t know when to stop training your model?
From Modeling to Scoring: Finding an Optimal Classification Threshold based on Cost and Profit
Click to learn more about co-author Maarit Widmann. Click to learn more about co-author Alfredo Roccato. Wheeling like a hamster in the Data Science cycle? Don’t know when to stop training your model? Model evaluation is an important part of a Data Science project and it’s exactly this part that quantifies how good your model is, how much it has improved from the previous version, how much better it is than your colleague’s model, and how much room for improvement there still is.
Easy Interpretation of a Logistic Regression Model with Delta-p Statistics
Key Takeaways With Delta-p statistics, the predictions based on a logistic regression model are easy to understand by non-technical decision-makers. Learn how to calculate the Delta-p statistics based on the coefficients of a logistic regression model for credit application processing. Data workflow includes the steps for accessing the raw data to training the logistic regression model, and evaluating the effects of individual predictor columns with Delta-p statistics.
Cohen’s Kappa: What It Is, When to Use It, and How to Avoid Its Pitfalls
Maarit Widmann Maarit Widmann is a data scientist at KNIME. She started with quantitative sociology and holds her bachelor’s degree in social sciences. The University of Konstanz made her drop the “social” as a Master of Science. Her ambition is to communicate the concepts behind data science to others in videos and blog posts. Follow Maarit on LinkedIn. For more information on KNIME, please visit www.knime.com and the KNIME blog.
From Modeling to Scoring: Confusion Matrix and Class Statistics
Click to learn more about author Maarit Widmann. Wheeling like a hamster in the Data Science cycle? Don’t know when to stop training your model? Model evaluation is an important part of a data science project and it’s exactly this part that quantifies how good your model is, how much it has improved from the previous version, how much better it is than your colleague’s model, and how much room for improvement there still is.
3 New Techniques for Data-Dimensionality Reduction in Machine Learning
Maarit Widmann Maarit Widmann is a junior data scientist at KNIME. She started with quantitative sociology and holds her bachelor’s degree in social sciences. The University of Konstanz made her drop the “social” as a Master of Science. Her ambition is to communicate the concepts behind data science to others in videos and blog posts. Follow Maarit on LinkedIn. The full big data explosion has convinced us that more is better.
How to Build Pivot Tables
Save {{ articles[0].views | formatCount}} Views Join the DZone community and get the full member experience. Join For Free Did you know that a pivot table allows you to quickly summarize your data based on a group, pivot, and aggregation columns? This summary might include sums, averages, or other statistics, which the pivot table splits the statistics is a meaningful way for different subgroups and draws attention to useful information. Fig.
Four Techniques for Outlier Detection
By Maarit Widmann, Moritz Heine, Rosaria Silipo, Data Scientists at KNIME Anomalies, or outliers, can be a serious issue when training machine learning algorithms or applying statistical techniques. They are often the result of errors in measurements or exceptional system conditions and therefore do not describe the common functioning of the underlying system. Indeed, the best practice is to implement an outlier removal phase before proceeding with further analysis.
Four Techniques for Outlier Detection
Ever been skewed by the presence of outliers in your set of data? Anomalies, or outliers, can be a serious issue when training Machine Learning algorithms or applying statistical techniques. They are often the result of errors in measurements or exceptional system conditions and therefore do not describe the common functioning of the underlying system. Indeed, the best practice is to implement an outlier removal phase before proceeding with further analysis.
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