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Articles related to "let"


Classifying Rare Events Using Five Machine Learning Techniques

  • Supervised learning is the machine learning task or process of producing a function that predicts output variables.
  • Which supervised learning technique works the best?
  • Data scientists have tasked with the sell team to come up with statistical solutions to identify future subscribers.
  • Next, we split the dataset into two parts: training and test sets.
  • This post compares 5 supervised methods: Logistic Regression, Decision Tree, Random Forest, KNN, and SVM.
  • For each method, we train a ML model and record down the training and test errors, respectively.
  • Following the same step, we find the training and test errors.
  • Let’s check the training and test errors of each method.
  • As we see here, Random Forests have the minimal training error while do not perform better in the test error.
  • To determine the best method, let’s adopt ROC and AUC.

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Use middleware in Next.js without custom server

  • APIs by exporting functions of two arguments req and res, which are extensions to Node's http.ClientRequest and http.ServerResponse.
  • The concept allowed us to augmented req and res by routing them through layers of a stack, which are known as middleware.
  • In those cases, the libraries actually return functions of (req, res, next) just like the way we approached above.
  • I will define handler function as the function of (req, res) that we need to export for API Routes.
  • Looking at the function withDatabase, it accepts an argument called handler, our original function.
  • Now that we have augmented req, we want to route it through our original handler.
  • Looking at return handler(req, res);, we are calling the original handler function we retrieve as an argument with the augmented req and (eh, unchanged) res.

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Classifying Rare Events Using Five Machine Learning Techniques

  • Supervised learning is the machine learning task or process of producing a function that predicts output variables.
  • Which supervised learning technique works the best?
  • Data scientists have tasked with the sell team to come up with statistical solutions to identify future subscribers.
  • Next, we split the dataset into two parts: training and test sets.
  • This post compares 5 supervised methods: Logistic Regression, Decision Tree, Random Forest, KNN, and SVM.
  • For each method, we train a ML model and record down the training and test errors, respectively.
  • Following the same step, we find the training and test errors.
  • Let’s check the training and test errors of each method.
  • As we see here, Random Forests have the minimal training error while do not perform better in the test error.
  • To determine the best method, let’s adopt ROC and AUC.

save | comments | report | share on


Classifying Rare Events Using Five Machine Learning Techniques

  • Supervised learning is the machine learning task or process of producing a function that predicts output variables.
  • Which supervised learning technique works the best?
  • Data scientists have tasked with the sell team to come up with statistical solutions to identify future subscribers.
  • Next, we split the dataset into two parts: training and test sets.
  • This post compares 5 supervised methods: Logistic Regression, Decision Tree, Random Forest, KNN, and SVM.
  • For each method, we train a ML model and record down the training and test errors, respectively.
  • Following the same step, we find the training and test errors.
  • Let’s check the training and test errors of each method.
  • As we see here, Random Forests have the minimal training error while do not perform better in the test error.
  • To determine the best method, let’s adopt ROC and AUC.

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Exploratory Data Analysis in R for beginners (Part 2)

  • However, to make our plots, charts and graphs more informative and of course visually appealing, we need to make one step further.
  • But before we even move on to what arguments and functions to use, we need to determine what kind of data frame/structure the dataset should be.
  • By looking at this plot, we can see that the data frame must have 1 column of 3 categories (Maths, Reading and Science), 1 column of numeric results for the % difference in performance in each subject and of course, 1 column for the country name.
  • The answer is NO: Caption, titles and subtitles are too small, the proportion and size of the plot is not as good as the plot we introduced at the start of this section.
  • As you can see, the last column of the data frame shows that Jordan is an outlier.

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CSS3 in 10 days — Day 4

  • Open your code editor and create a new 4.1-8BitMario folder and two files index.html and sandbox.css inside it.
  • Let’s create mario now.
  • Head over to sandbox.css and copy the below code.
  • We are giving background-color: #e7eef1 and some box-shadow also as #e7eef1.
  • The above will result in showing a little part of Mario’s hat.
  • Let’s complete the Mario by putting all the code.
  • Add the below code in sandbox.css and then click and hold the Wahoo!
  • Open your code editor and create a new 4.2-PricingTable folder and two files index.html and sandbox.css inside it.
  • It will result in below.
  • Open your code editor and create a new 4.3-CSSVariables folder and two files index.html and sandbox.css inside it.
  • Now, put the variable declaration in sandbox.css .
  • It will show the boxes in browser.

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CSS3 in 10 days — Day 5

  • We will start with CSS only Modal Window on day-5.
  • Open your code editor and create a new 5.1-ModalWindow folder and two files index.html and sandbox.css inside it.
  • It will show our index.html as the below in browser.
  • So, head over to sandbox.css and put the below code.
  • So, now if you click on the button the overlay will cover the whole browser.
  • Next, we will create a CSS only Pacman.
  • Open your code editor and create a new 5.2-Pacman folder and two files index.html and sandbox.css inside it.
  • Let’s first create the path for the Pacman.
  • Let’s create the pacman now.
  • We will now see the pacman in the browser.
  • Now, let’s animate the pacman close and open it’s mouth.
  • Let’s create the Ghost now.
  • Put the below in sandbox.css to create the ghost.
  • This completes our pacman animation.

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An Adequate Introduction to Functional Programming

  • Well, the problem basically boils down to this idea: with shared state in order to understand the effects of a function, you have to know the entire history of every shared variable that the function uses or affects.
  • Another way to put this problem is: functions/operations/routines that act on shared state are time and order dependent.
  • Indeed, the functional solution to this problem is to handle state in a single (large) object “outside” the application, updated with an immutable approach (so cloned and updated each time).
  • In the front-end development field, this pattern is adopted and implemented with so-called state-managers such as Redux, Vuex and NgRx. At a cost of more code (not so much) and complexity, our applications will become more predictable, manageable and maintainable.

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7 Basic Tools of Quality using R

  • Ishikawa’s seven basic tools for quality correspond to a fixed set of graphical and statistical techniques helpful in solving critical quality related issues.
  • It represents an extremely useful tool during the first stages of analyzing a problem since it helps visualize what problems need attention first by looking at the tallest bars of the chart, which represent the variables with the greatest cumulative effect on a given system.
  • Also known as the Ishikawa or fishbone diagram, the cause-and-effect diagram represents a tool that helps identify potential root causes for an effect, problem or undesirable outcome while sorting them into six major categories commonly referred as the six Ms: measurement, material, machine, method, man power and mother nature.
  • Some of the advantages if using check sheets include: effective way of displaying data; easy to use; can identify the root cause of a problem; can be used to substantiate or refute allegations; and represent the first step in the construction of other graphical tools.

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7 Basic Tools of Quality using R

  • Ishikawa’s seven basic tools for quality correspond to a fixed set of graphical and statistical techniques helpful in solving critical quality related issues.
  • It represents an extremely useful tool during the first stages of analyzing a problem since it helps visualize what problems need attention first by looking at the tallest bars of the chart, which represent the variables with the greatest cumulative effect on a given system.
  • Also known as the Ishikawa or fishbone diagram, the cause-and-effect diagram represents a tool that helps identify potential root causes for an effect, problem or undesirable outcome while sorting them into six major categories commonly referred as the six Ms: measurement, material, machine, method, man power and mother nature.
  • Some of the advantages if using check sheets include: effective way of displaying data; easy to use; can identify the root cause of a problem; can be used to substantiate or refute allegations; and represent the first step in the construction of other graphical tools.

save | comments | report | share on