Data points are the information we feed into the machine learning model. The number of data points, or the volume of data, determines the accuracy of the model.

What is the volume of data in a machine learning model? The volume of data is the number of different inputs we give to the machine learning model.

Let’s talk about how to determine the accuracy of the model. The more input data points there are, the more accurate the model becomes.

If you have a small amount of data, a very simple model will be able to accurately describe the data. If you have a large amount of data, a more complicated model will be able to accurately describe the data.

When and why do we need to consolidate data points?

Consolidating data points is a way to reduce the amount of data you need to feed into the machine learning model in order to make it more accurate.

For example, if you have two years of income tax returns for a single person, you can probably just combine the years together into one data point, saving yourself a lot of time and effort.

The most common machine learning model is a linear regression model. Linear Regression is a very simple model that assumes that the relationship between the input and the output is a straight line. The more complicated models are neural networks, which are models that are inspired by the way the brain works.

What are data points used for? An example of a data point in a machine learning model would be the amount of money a person makes each year. The amount of money a person makes each year is the input data for the linear regression model.

Let’s start by defining what linear regression is.

Linear Regression is a way to find the relationship between two variables. Your first step is to decide what age range you want to work with. In this case, the two variables are annual income and race.

The linear regression equation we use is:

Annual Income = a+b* Race+c* Gender+d* Age

The goal is to find the value of a,b, c, and d. In this case, the goal is to find out how much people make based on their race, gender, and age.

What is a data point in logistic regression?

As the Wikipedia page for logistic regression has noted,

A data point in statistics usually represents an observation that can be expressed as a pair (x,y), where x is a vector of features and y is a binary class.

So my first question is:
Is a logical what you get when you calculate a probability of 1 from a probability of 0 with given feature vectors in a logistic regression model?

That would imply that the logistic regression algorithm is based on the calculation of probabilities of observations being one (1) or zero (0). What was traditionally called a “classifier” function.

Please read how the Logistic Regression algorithm works.

Now you know what a data point is. A data point is a discrete informational unit. In a broad sense, a data point is a single fact. Data point is essentially synonymous with datum, the singular form of data.

If you are good at SEO, you can collect data for your dataset using Netpeak Spider or SiteBulb.

If you’re a newbie to ML and data mining, you may try Orange Software datasets.

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Let’s walk through a basic example and use it as an excuse to talk about the process of getting answers from your data using machine learning.

7 simple steps of Machine Learning

Let’s pretend that we’ve been asked to create a system that answers the question of whether a drink is wine or beer. This question answering system that we build is called a model, and this model is created via a process called training. In machine learning, the goal of training is to create an accurate model that answers our questions correctly most of the time. But in order to train the model, we need to collect data to train on. This is where we will begin.

Our data will be collected from glasses of wine and beer. There are many aspects of drinks that we could collect data on everything from the amount of foam to the shape of the glass. But for our purposes, we’ll just pick two simple ones – the color as a wavelength of light and the alcohol content as a percentage. The hope is that we can split our two types of drinks along these two factors alone.

We’ll call these our features from now on color and alcohol. The first step to our process will be to run out to the local grocery store, buy up a bunch of different drinks, and get some equipment to do our measurements a spectrometer for measuring the color and a hydrometer to measure the alcohol content. It appears that our grocery store has an electronics hardware section as well.

Once our equipment and then booze we got it all set up it’s time for our first real step of machine learning.

Gathering that data

This step is very important because the quality and quantity of data that you gather will directly determine how good your predictive model can be.

In this case, the data we collect will be the color and alcohol content of each drink. This will yield us a table of color, alcohol content, and whether it’s beer or wine.

This will be our training data. So a few hours of measurements later, we’ve gathered our training data and had a few drinks, perhaps. And now it’s time for our next step of machine learning

Data preparation

Where we load our data into a suitable place and prepare it for use in our machine learning training.

We’ll first put all our data together then randomize the ordering. We wouldn’t want the order of our data to affect how we learn since that’s not part of determining whether a drink is beer or wine.

In other words, we want to make a determination of what a drink is independent of what drink came before or after it in the sequence. This is also a good time to do any pertinent visualizations of your data, helping you see if there is any relevant relationships between different variables as well as show you if there are any data imbalances.

For instance, if we collected way more data points about beer than wine, the model we train will be heavily biased toward guessing that virtually everything that it sees is beer since it would be right most of the time.

However, in the real world, the model may see beer and wine in equal amount, which would mean that it would be guessing beer wrong half the time. We also need to split the data into two parts.

The first part used in training our model will be the majority of our dataset.

The second part will be used for evaluating our train model’s performance. We don’t want to use the same data that the model was trained on for evaluation since then it would just be able to memorize the questions, just as you wouldn’t want to use the questions from your math homework on the math exam.

Sometimes the data we collected needs other forms of adjusting and manipulation– things like duplication, normalization, error correction, and others. These would all happen at the data preparation step. In our case, we don’t have any further data preparation needs, so let’s move on forward.

The next step in our workflow is choosing a model

There are many models that researchers and data scientists have created over the years. Some are very well suited for image data, others for sequences, such as text or music, some for numerical data, and others for text-based data. In our case, we have just two features – color and alcohol percentage.

We can use a small linear model, which is a fairly simple one that will get the job done. Now we move on to what is often considered the bulk of machine learning

The training

In this step, we’ll use our data to incrementally improve our model’s ability to predict whether a given drink is wine or beer. In some ways, this is similar to someone first learning to drive.

At first, they don’t know how any of the pedals, knobs, and switches work or when they should be pressed or used. However, after lots of practice and correcting for their mistakes, a licensed driver emerges.

Moreover, after a year of driving, they’ve become quite adept at driving. The act of driving and reacting to real-world data has adapted their driving abilities, honing their skills. We will do this on a much smaller scale with our drinks. In particular, the formula for a straight line is y equals mx plus b, where x is the input, m is the slope of the line, b is the y-intercept, and y is the value of the line at that position x.

The values we have available to us to adjust or train are just m and b, where the m is that slope and b is the y-intercept. There is no other way to affect the position of the line since the only other variables are x, our input, and y, our output. In machine learning, there are many m’s since there may be many features.

The collection of these values is usually formed into a matrix that is denoted w for the weights matrix. Similarly, for b, we arranged them together, and that’s called the biases. The training process involves initializing some random values for w and b and attempting to predict the outputs with those values.

As you might imagine, it does pretty poorly at first, but we can compare our model’s predictions with the output that it should have produced and adjust the values in w and b such that we will have more accurate predictions on the next time around. So this process then repeats.

Each iteration or cycle of updating the weights and biases is called one training step. So let’s look at what that means more concretely for our dataset. When we first start the training, it’s like we drew a random line through the data.

Then as each step of the training progresses, the line moves step by step closer to the ideal separation of the wine and beer. Once training is complete, it’s time to see if the model is any good.

Using evaluation, this is where that dataset that we set aside earlier comes into play. Evaluation allows us to test our model against data that has never been used for training. This metric allows us to see how the model might perform against data that it has not yet seen. This is meant to be representative of how the model might perform in the real world.

Training-evaluation split

A good rule of thumb I use for a training-evaluation split is somewhere on the order of 80%-20% or 70%-30%. Much of this depends on the size of the original source dataset.

If you have a lot of data, perhaps you don’t need as big of a fraction for the evaluation dataset.

Parameter tuning

Once you’ve done evaluation, it’s possible that you want to see if you can further improve your training in any way. We can do this by tuning some of our parameters. There were a few that we implicitly assumed when we did our training, and now is a good time to go back and test those assumptions, try other values.

One example of a parameter we can tune is how many times we run through the training set during training. We can actually show the data multiple times. So by doing that, we will potentially lead to higher accuracies.

Another parameter is learning rate. This defines how far we shift the line during each step based on the information from the previous training step. These values all play a role in how accurate our model can become and how long the training takes.

For more complex models, initial conditions can play a significant role as well in determining the outcome of training. Differences can be seen depending on whether a model starts off training with values initialized at zeros versus some distribution of the values and what that distribution is.

As you can see, there are many considerations at this phase of training, and it’s important that you define what makes a model good enough for you. Otherwise, we might find ourselves tweaking parameters for a very long time. Now, these parameters are typically referred to as hyperparameters.

The adjustment or tuning of these hyperparameters still remains a bit more of an art than a science, and it’s an experimental process that heavily depends on the specifics of your dataset, model, and training process. Once you’re happy with your training and hyperparameters, guided by the evaluation step, it’s finally time to use your model to do something useful.

Prediction

Machine learning is using data to answer questions, so prediction or inference is that step where we finally get to answer some questions. This is the point of all of this work where the value of machine learning is realized. We can finally use our model to predict whether a given drink is wine or beer, given its color and alcohol percentage.

The power of machine learning is that we were able to determine how to differentiate between wine and beer using our model rather than using human judgment and manual rules.

You can extrapolate the ideas presented today to other problem domains as well, where the same principles apply.

If you’re looking for more ways to play with training and parameters, check out the BigML. It’s a completely browser-based machine learning tool, where you can try different parameters and run training against mock datasets.

And don’t worry, you can’t break the site. Of course, we will encounter more steps and nuances in future episodes, but this serves as a good foundational framework to help us think through the problem, giving us a common language to think about each step and go deeper in the future.

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Explanation of what is the difference between supervised and unsupervised machine learning.

In supervised machine learning algorithms, we have to provide labelled data, for example, prediction of stock market prices, whereas in unsupervised we need not have labelled data, for example, classification of emails into spam and non-spam.

The difference is that if we feed unlabelled data into a supervised algorithm, it might misclassify some data. For example, if we classify all emails by the content, rather than the sender’s name, we may incorrectly classify a message containing human-readable information as spam.

So, in unsupervised learning algorithms, we have to first identify the features that allow classifying an email as spam and then feed in that data to the algorithm.

Is supervised learning more difficult to implement?

Supervised learning is definitely more difficult to implement as the algorithms used to carry out the calculations are highly complicated. But as the datasets become larger, using unsupervised learning algorithms becomes more efficient because you can efficiently use more data to carry out the same calculation.

In unsupervised learning algorithms, you only need a few samples or training data, and if the data is good enough to detect a pattern, the algorithm will converge to the correct answer.

What are some advantages of unsupervised learning?

On one hand, it is more efficient, meaning that an algorithm can process more data in a shorter period of time.

On the other hand, we do not need to share or transfer labelled data with other people because the unsupervised algorithm can learn by itself. That is why unsupervised learning is essential for AI applications.

Which universities around the world are leading the way?

There are more than a hundred universities around the world that are developing unsupervised learning.

China and the United States are the leading players, and we see that the major companies in the AI space use unsupervised learning algorithms.

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It is clear that validating machine learning models is a very difficult task. For instance, how would one validate an advanced artificial intelligence (AI) system? The AI might be able to answer many questions based on its vast knowledge and experience. However, it may not always be possible for a human to understand the methodologies used by the AI in arriving at certain conclusions or predictions.

In machine learning, model validation is referred to as the process where a trained model is evaluated with a testing data set. The testing data set is a separate portion of the same data set from which the training set is derived.

The basic recipe for applying a supervised machine learning model are:
* Choose a class of model
* Choose model hyper parameters
* Fit the model to the training data
* Use the model to predict labels for new data
From Python Data Science Handbook by Jake VanderPlas

In my opinion, cross-validation is the best technique for evaluating the efficiency of an individual data set on a statistical prediction model. The goal is to make sure that the model and the data fit together well. During the training process, cross-validation is performed where the user can determine whether the model is prone to underfitting or overfitting the data. For the goal variable, the data to be used for cross-validation must be from the same distribution, or else we will confuse ourselves on how the model will behave in real life.

The best way to validate Machine Learning models is through real-time data.

As the ML model learns from data, it will keep on updating itself to reflect new information.

If we have real-time data from the environment, the ML model should be able to predict what is going to happen next.

If the model can make predictions, then we validate it.

If the predictions turn out to be inaccurate, then we can change the ML model and make it more accurate.

Imagine that there is a situation where we want to predict the future stock prices of Google, Microsoft, and Apple or consider a simple machine learning model that predicts the movement of a ball as it drops.

Given its very limited experience with such predictions, one might say that this particular machine learning model is only correct 50% of the time. However, if this same model were used to make predictions regarding the flight path of an airplane in real-time, then such inaccuracy could have serious consequences.

It is also worth mentioning that the human mind itself can only make predictions based on its own experiences. And yet, a human might think that his or her predictions are always accurate. In fact, we often hear people say things like, ‘I knew it all along’ or ‘it had to happen this way.’ However, these behaviors do not indicate true knowledge and understanding at all.

In summary, humans often do not fully understand the nature of knowledge and belief. They can never really know if their conclusions are correct or incorrect.

An advanced AI system may be able to deduce certain conclusions and make predictions with greater accuracy than a human ever could. However, if such an AI were to claim certainty in its knowledge, one might say that it is only correct 80% of the time.

In fact, we might say that humans are correct 10% of the time, while an advanced AI is only correct 100% of the time. Of course, this assumes there is no chance for error in the system’s data or algorithms.

I think it is rather difficult to assess exactly how accurate models are. We can use standard statistical tools for assessing model accuracy of course, but I feel that these do not account for all the relevant considerations.

I am not sure whether all those considerations are really relevant to assessing model accuracy and I would like to know what other people think about this.

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