AI News, In Machine Learning, What is Better: More Data or better Algorithms

In Machine Learning, What is Better: More Data or better Algorithms

This quote is usually linked to the article on “The Unreasonable Effectiveness of Data”, co-authored by Norvig  himself (you should probably be able to find the pdf on the web although the original is behind the IEEE paywall).

The last nail on the coffin of better models is when Norvig is misquoted as saying that “All models are wrong, and you don’t need them anyway”

But, in order to understand why, we need to get slightly technical.  (I don’t plan on giving a full machine learning tutorial in this post.

In both cases, the authors were  working on language models in which roughly every word in the vocabulary makes a feature.

In that case, known as high bias, adding more data will not help.  See below a plot of a real production system at Netflix and its performance as we add more training examples.

More features to the rescue If you are with me so far, and you have done your homework in understanding high variance and high bias problems, you might be thinking that I have deliberately left something out of the discussion.

Pretty early on in the game, there was a blog post by serial entrepreneur and Stanford professor Anand Rajaraman commenting on the use of extra features to solve the problem.

As a matter of fact, many teams showed later that adding content features from IMDB or the like to an optimized algorithm had little to no improvement.

Some of the members of the Gravity team, one of the top contenders for the Prize, published a detailed paper in which they showed how those content-based features would add no improvement to the highly optimized collaborative filtering matrix factorization approach.

For example, I have seen people invest a lot of effort in implementing distributed Matrix Factorization when the truth is that they could have probably gotten by with sampling their data and gotten to very similar results.

Of course, whenever there is a heated debate about a possible paradigm change, there are people like Malcolm Gladwell or Chris Anderson that make a living out of heating it even more (don’t get me wrong, I am a fan of both, and have read most of their books).

The article explains several examples of how the abundance of data helps people and companies take decision without even having to understand the meaning of the data itself.

And the result is a set of false statements, starting from the title: the data deluge does not make the scientific method obsolete.

But, overall, what we need is good approaches that help us understand how to interpret data, models, and the limitations of both in order to produce the best possible output.

8 Proven Ways for improving the “Accuracy” of a Machine Learning Model

But, if you follow my ways (shared below), you’d surely achieve high accuracy in your models (given that the data provided is sufficient to make predictions).

In this article, I’ve shared the 8 proven ways using which you can create a robust machine learning model.

The model development cycle goes through various stages, starting from data collection to model building.

This practice usually helps in building better features later on, which are not biased by the data available in the data-set.

The unwanted presence of missing and outlier values in the training data often reduces the accuracy of a model or leads to a biased model.

It shows that, in presence of missing values, the chances of playing cricket by females is similar as males.

But, if you look at the second table (after treatment of missing values based on salutation of name, “Miss”

This step helps to extract more information from existing data. New information is extracted in terms of new features. These features may have a higher ability to explain the variance in the training data.

Feature Selection is a process of finding out the best subset of attributes which better explains the relationship of independent variables with target variable.

Hitting at the right machine learning algorithm is the ideal approach to achieve higher accuracy.

To tune these parameters, you must have a good understanding of these meaning and their individual impact on model. You can repeat this process with a number of well performing models.

This technique simply combines the result of multiple weak models and produce better results. This can be achieved through many ways: To know more about these methods, you can refer article “Introduction to ensemble learning“.

Till here, we have seen methods which can improve the accuracy of a model. But, it is not necessary that higher accuracy models always perform better (for unseen data points). Sometimes, the improvement in model’s accuracy can be due to over-fitting too.

To know more about this cross validation method, you should refer article “Improve model performance using cross validation“.

Once you get the data set, follow these proven ways and you’ll surely get a robust machine learning model. But, these 8 steps can only help you, after you’ve mastered these steps individually.

If you need any more help with machine learning models, please feel free to ask your questions in the comments below.

How to use Excel's Data Model to turn related data into meaningful information

Excel can analyze mountains of data, but you might be working too hard if you're not utilizing the Data Model feature to corral it.

This feature lets you integrate data from multiple tables by creating relationships based on a common column.

The model works behind the scenes and simplifies PivotTable objects and other reporting features.

In this article, I'll show you how to create a PivotTable using data from two tables by using the Data Model feature to create a relationship between the two tables before building the PivotTable.

Now let's suppose you're working for a large grocery franchise and you want to analyze shelving data.

So, you import a table of shelving codes that includes a helpful description, but how do you add the description with each record?

Using Excel's Data Model feature, we'll display the description field instead of the shelf code when grouping and analyzing the values without using VLOOKUP() or any other functions.

If you're unfamiliar with the term, a relationship connects two sets of data by a common column (field) of values.

Each person occurs multiple times, each fruit appears multiple times, even the months and shelf codes appear multiple times.

Each shelf code occurs only once in the lookup table, but it can occur multiple times in the produce data set.

In a nutshell, we're looking up a value in the lookup data set to display with the produce records.

Excel combines the data, based on the Shelf Code field, in the Data Model, which contains the data and the relationships, but you won't see it.

What's important to note at this point, is that the Data Model solution requires substantially less memory than a sheet full of expressions using the LOOKUP() function!

Don't worry—the benefits of the feature (known as Power Pivot) are still available, but you can't view the combined tables.

If you click Diagram View in the View group, you'll see a diagram of the one-to-many relationship between the two Table objects, as shown in Figure F.

Now, lets create a PivotTable that counts the number of times each person shelved items (it's a contrived example, but it's simple and doesn't add unnecessary steps).

(Excel will default to a count.) On the Design tab, I chose Light Blue, Pivot Style Light 9 to distinguish the data from the labels.

At first, it seems like you've simply traded chores—creating a relationship instead of adding the VLOOKUP() function, but that's because the example is simple.

For example, 'Please troubleshoot my workbook and fix what's wrong' probably won't get a response, but 'Can you tell me why this formula isn't returning the expected results?'

I'm not reimbursed by TechRepublic for my time or expertise when helping readers, nor do I ask for a fee from readers I help.

Data sources for the Power BI service

Whenever you're exploring data, creating charts and dashboards, asking questions with Q&A, all of those visualizations and answers you see are really getting their underlying data from a dataset.

Excel (.xlsx, xlxm) � Excel is unique in that a workbook can have both data you've entered into worksheets yourself, and you can query and load data from external data sources by using Power Query (Get &

You can import data that is in tables in worksheets (the data must be in a table), or import data that is loaded into a data model.

Power BI Desktop (.pbix) - You can use Power BI Desktop to query and load data from external data sources, extend your data model with measures and relationships, and create reports.

Power BI Desktop is best for more advanced users who have a good understanding of their data sources, data query and transformation, and data modeling concepts.

For example, a .csv containing name and address data can have a number of rows where each row has values for first name, last name, street address, city, state, and so on.

You cannot import data into a .csv file, but many applications, like Excel, can save simple table data as a .csv file.

Connections from Power BI to these databases are live, that is, when you've connected to say an Azure SQL Database, and you begin exploring its data by creating reports in Power BI, anytime you slice your data or add another field to a visualization, a query is made right to the database.

If you setup scheduled refresh, Power BI will use connection information from the file along with refresh settings you configure to connect directly to the datasource and query for updates.

But regardless of where you get your data from, that data has to be in a format the Power BI service can use to create reports and dashboards, answer questions with Q &

Some data sources already have their data in a format ready for the Power BI service, like content packs from service providers like Google Analytics, and Twilio.

If you setup scheduled refresh or do a manual refresh on the dataset, Power BI will use the connection information from the dataset, along with a couple other settings, to connect directly to the database, query for updates, and load those updates into the dataset.

dataset is automatically created in Power BI when you use Get Data to connect to and import data from a content pack, file, or you connect to a live data source.

A dataset contains information about the data source, data source credentials, and in many cases, a sub-set of data copied from the data source.

For example, an online service like Google Analytics or QuickBooks, a database in the cloud like Azure SQL Database, or a database or file on a local computer or server in your own organization.

If you save your files on your local drive, or a drive somewhere in your organization, a Power BI gateway might be required in-order to refresh the dataset in Power BI.

Content packs from others in your organization will depend on the data sources used and how the person who created the content pack setup refresh.

Create a memory-efficient Data Model using Excel and the Power Pivot add-in

In Excel 2013 or later, you can create data models containing millions of rows, and then perform powerful data analysis against these models.

For workbook data models that contain millions of rows, you’ll run into the 10 MB limit pretty quickly.

Taking the time to learn best practices in efficient model design will pay off down the road for any model you create and use, whether you’re viewing it in Excel 2013, Office 365 SharePoint Online, on an Office Web Apps Server, or in SharePoint 2013.

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