ml vs human

Machine learning methods have several advantages over traditional methods for
predictions:

Handling complex and large datasets: Machine learning algorithms can handle
large and complex datasets that are difficult for traditional methods to
process. These algorithms can identify patterns and relationships within the
data that may be difficult to detect through traditional methods.

Adaptability: Machine learning models can adapt and improve their predictions
as they receive more data. They can learn from experience, making them ideal
for scenarios where the underlying patterns or relationships may change over
time.

Automation: Machine learning algorithms can automate the prediction process,
reducing the need for manual intervention. This saves time and resources,
making it ideal for organizations that need to process large volumes of data.

Scalability: Machine learning algorithms can scale to handle large amounts of
data and can be deployed in a distributed environment, making them ideal for
scenarios where predictions need to be made quickly and efficiently.

Accuracy: Machine learning algorithms can achieve higher levels of accuracy
than traditional methods, especially in scenarios where the underlying patterns
or relationships are complex or nonlinear. This makes them ideal for
applications where accuracy is critical, such as in medical diagnosis or fraud
detection.

Overall, machine learning methods offer a range of benefits over traditional
methods for predictions, making them an increasingly popular choice in a wide
range of industries and applications

-**

7 reasons why ML for forecasting is better than traditional methods
Let's take a look at seven reasons why machine learning is a better predictor
than traditional methods.

1. Machine learning can identify patterns that are too complex for humans to
observe.
One of the key advantages of machine learning is that it can identify patterns
that are too complex for humans to observe. Traditional methods of forecasting
are limited by the amount of data that can be processed and analyzed by humans.

For example, suppose we wanted to forecast stock market prices. Traditional
methods would rely on analysts to identify patterns in the market and make
predictions based on research. However, it is often difficult for humans to
identify all of the factors that influence stock prices. Machine learning can
analyze large amounts of data very quickly and identify patterns that are not
visible to humans. This can lead to more accurate predictions than traditional
methods.

Renaissance Technologies has used machine learning to great effect in this
area. The company has developed machine learning algorithms that have achieved
over 70% annualized returns since its inception in 1994.

2. Machine learning can make predictions based on a much larger data set than
traditional methods.
Machine learning can also make predictions based on a much larger data set than
traditional methods.

Consider the problem of forecasting sales. A traditional method such as trend
analysis might only consider past sales data in order to make a forecast.
Machine learning, on the other hand, can analyze data from social media,
customer reviews, and other sources in order to make a more accurate prediction.

In addition to time series data, machine learning models can factor in supply
chain data and other real-world metrics, enabling greater demand forecasting
accuracy. Traditional time series forecasting falls short when it comes to big
data.

3. Machine learning is not as biased by human emotions or subjective opinions.
One of the biggest disadvantages of traditional methods of forecasting is that
they are biased by human emotions and subjective opinions. This can lead to
inaccurate predictions, as humans are often swayed by their personal biases and
emotions. Machine learning is not as biased by human emotions or subjective
opinions, which leads to more accurate predictions.

Consider the example of a company that is considering opening a new store.
Traditional methods of forecasting might be biased by the personal biases of
the people doing the forecasting. For example, they may be more likely to
predict that the store will be successful if they are personally invested in
it, regardless of the evidence. Machine learning, on the other hand, would not
be swayed by these personal biases and would make more accurate predictions.

Of course, ML models can be biased as well, if the data used to train the
models has bias. However, after ensuring that you’re using unbiased data, you
can rely on cross-validation to inform you if the model you’re building is
accurate.

4. Machine learning can adapt to changes quickly
Machine learning can also adapt to changes in the data set, whereas traditional
methods can become less accurate over time. As the data set changes, machine
learning will adapt its predictions accordingly. This ensures that the
predictions are always accurate and up-to-date. Traditional methods, on the
other hand, can become less accurate over time as the data set changes.

For instance, let's say you have a data set that consists of customer purchase
data. As time goes on, the customers in this data set may change. The
traditional approach would be to rebuild the forecast with the new data set,
which would then produce new predictions. However, if you use machine learning,
the model can automatically adapt to the new data set.

5. Machine learning is not as easily manipulated as traditional methods.
Machine learning is also less easily manipulated than traditional methods. As
machine learning relies on algorithms to make predictions, it is much more
difficult to manipulate the predictions than it is to manipulate the
predictions made by traditional methods. This leads to more accurate
predictions.

6. Machine learning is a more efficient use of resources
Machine learning is a more efficient use of resources than traditional methods.
Traditional methods often require a lot of manual work, which can be
time-consuming and expensive. The modern executive understands that to remain
competitive, they need to focus on leveraging technology for competitive
advantage. Machine learning can automate the process of making predictions,
which is a more efficient use of resources.

7. Machine learning is more accessible than traditional methods
Machine learning is also more accessible than traditional methods. Traditional
methods often require specialized knowledge and training. Machine learning, on
the other hand, is becoming more accessible as the technology advances. There
are now many software platforms that allow anyone to build machine learning
models without any prior knowledge or experience.

How does machine learning forecasting work?
There are four main steps in the machine learning forecasting process: data
gathering, data pre-processing, model training, and model evaluation.

Naturally, the first step is data gathering, since data fuels all machine
learning models. Data mining refers to the process of collecting and analyzing
historical data from various sources, whether it’s scraping the web, extracting
information from forms, or just relevant Excel sheets. Time series models are
picky about data formatting, so there need to be clear “time steps” in the data.

Data preprocessing cleans and prepares the data for use in the machine learning
algorithm. This step includes things like removing noisy data, standardizing
data, feature engineering, and transforming data into a format that the
algorithm can understand. Even traditional statistical methods require data
pre-processing.

Traditionally, technical talent was needed to perform data pre-processing with
tools like Python. However, with the advent of self-service platforms like
Akkio, business users can now easily clean and prepare their data without help
from IT. This has increased the adoption of machine learning forecasting in
business settings.

Once the data is ready, the machine learning algorithm is trained on it. This
involves selecting a model type and configuring its parameters. Once the model
is trained, it is put to use by forecasting future events. The performance of
the model is then evaluated by comparing its predictions against actual
outcomes.

Akkio builds a number of machine learning models in the background for any
given problem to maximize accuracy. Depending on the dataset, this includes
decision trees, ARIMA models, long short-term memory networks, recurrent neural
networks (RNNs), LSTMs, and other deep learning techniques. Various
optimization techniques are deployed across these machine learning methods,
enabling greater accuracy than if just one model was used.

Historically, companies would have to hire data scientists to use tools like
TensorFlow and Keras to build these models, but now any non-technical business
professional can build and deploy models in clicks. Data science professionals
can also benefit from Akkio’s methodology with faster experimentation and
deployment.

Once the problem goes beyond univariate and nonlinear problems, Akkio’s power
truly shines: Anyone can build highly complex supervised learning models in
moments.

Suppose we wanted to predict revenue for a company. The data pre-processing
step would involve removing any noisy data, such as errors in the sales data,
and standardizing the data so that all the values are of the same scale. The
model training step would involve finding patterns in the data to build a model
that can predict future revenue. The model evaluation step would involve
comparing the predictions of the model against actual revenue outcomes.

**
Advantages of Machine learning
1. Easily identifies trends and patterns
Machine Learning can review large volumes of data and discover specific trends
and patterns that would not be apparent to humans. For instance, for an
e-commerce website like Amazon, it serves to understand the browsing behaviors
and purchase histories of its users to help cater to the right products, deals,
and reminders relevant to them. It uses the results to reveal relevant
advertisements to them.

Do you know the Applications of Machine Learning?

2. No human intervention needed (automation)
With ML, you don’t need to babysit your project every step of the way. Since it
means giving machines the ability to learn, it lets them make predictions and
also improve the algorithms on their own. A common example of this is
anti-virus softwares; they learn to filter new threats as they are recognized.
ML is also good at recognizing spam.

3. Continuous Improvement
As ML algorithms gain experience, they keep improving in accuracy and
efficiency. This lets them make better decisions. Say you need to make a
weather forecast model. As the amount of data you have keeps growing, your
algorithms learn to make more accurate predictions faster.

4. Handling multi-dimensional and multi-variety data
Machine Learning algorithms are good at handling data that are
multi-dimensional and multi-variety, and they can do this in dynamic or
uncertain environments.

5. Wide Applications
You could be an e-tailer or a healthcare provider and make ML work for you.
Where it does apply, it holds the capability to help deliver a much more
personal experience to customers while also targeting the right customers.

Disadvantages of Machine Learning
With all those advantages to its powerfulness and popularity, Machine Learning
isn’t perfect. The following factors serve to limit it:

1. Data Acquisition
Machine Learning requires massive data sets to train on, and these should be
inclusive/unbiased, and of good quality. There can also be times where they
must wait for new data to be generated.

2. Time and Resources
ML needs enough time to let the algorithms learn and develop enough to fulfill
their purpose with a considerable amount of accuracy and relevancy. It also
needs massive resources to function. This can mean additional requirements of
computer power for you.

Also, see the future of Machine Learning

3. Interpretation of Results
Another major challenge is the ability to accurately interpret results
generated by the algorithms. You must also carefully choose the algorithms for
your purpose.

4. High error-susceptibility
Machine Learning is autonomous but highly susceptible to errors. Suppose you
train an algorithm with data sets small enough to not be inclusive. You end up
with biased predictions coming from a biased training set. This leads to
irrelevant advertisements being displayed to customers. In the case of ML, such
blunders can set off a chain of errors that can go undetected for long periods
of time. And when they do get noticed, it takes quite some time to recognize
the source of the issue, and even longer to correct it.


**
ML methods are computationally more demanding than statistical ones. In many
cases, the explainability and interpretability of the models in ML methods may
not be fully clear. Yet in business applications with vast amounts of data, ML
techniques may be better suited for predictions due to the large number of data
features involved and the fact that the algorithm used may not be very linear
or straightforward.

In the case of predicting the rate of default for loan applications, the
forecast values might be impacted by several thousand factors depending on the
customer information. In such scenarios, ML algorithms can outperform
statistical methods. One of the added advantages of ML forecasting in this
scenario is that an ensemble of different forecasting techniques – both linear
and nonlinear – can be combined to achieve higher accuracy (figure 3).

Figure 3. The machine learning forecasting process
related-graphic-3-the-evolution-of-forecasting-techniques-traditional-versus-machine-learning-methods.jpg
Comparing traditional and ML forecasting
To illustrate the differences between traditional and ML forecasting methods,
let's explore a business case from a US consumer product goods company.

The model considers the weekly US sales forecast for a cereal manufacturer. The
comparison used a statistical forecast for weekly sales ($) using traditional
methods. On the other hand, an ensemble ML model was used simultaneously to
forecast the product sales ($).

The example is indicative of the differences between the two methodologies in
terms of explainability and model accuracy. This example can come to life by
delving into the individual predictor variables considered, including but not
limited to: month, week, number of days available to ship and transport the
product, the pricing of the product, and the sales of competitor products.

We can see the actual weekly sales of a cereal category in 2019 (figure 4),
along with the predictions made using traditional forecasting (ARIMAX) and an
ensemble ML forecast. The data shows weekly sales forecasting predicted values
from both approaches and actual weekly sales for the 12 weeks starting on April
1, 2019. We can also see the differences in error metrics between the two
approaches (table 1).


ML Vs Classical Algorithms
ML algorithms do not depend on rules defined by human experts. Instead, they
process data in raw form — for example text, emails, documents, social media
content, images, voice and video.
An ML system is truly a learning system if it is not programmed to perform a
task, but is programmed to learn to perform the task
ML is also more prediction-oriented, whereas Statistical Modeling is generally
interpretation-oriented. Not a hard and fast distinction especially as the
disciplines converge, but in my experience most historical differences between
the two schools of thought fall out from this distinction
In classical algorithms, statisticians emphasis on p-value more and a solid but
comprehensible model
Most ML models are uninterpretable, and for these reasons they are usually
unsuitable when the purpose is to understand relationships or even causality.
The mostly work well where one only needs predictions.
Traditional learning methodologies such as training a model-based on historic
training data and evaluating the resulting model against incoming data is not
feasible as the environment is in a constant change.
As compared to the classical approach, traditional ML approaches as in most
cases these approaches are too expensive within web scale environments and
their results are too static to cope with dynamically changing service
environments
As opposed to classical approach, spending a lot of computational power on
learning a very complex model of a highly dynamic network environment is not
cost-effective
Gradually, “statistical modelling” will move towards “statistical learning” and
employ good parts about and creating tools for better interpreting the models
in the process, Pekka Kohonen, assistant professor at the Karolinska Institutet
pointed out
One of the key differences is that classical approaches have a more rigorous
mathematical approach while machine learning algorithms are more data-intensive
In the last two decades, there has been a significant growth in algorithmic
modeling applications, which has happened outside the traditional statistics
community. Young computer scientists are relying on machine learning which is
producing more reliable information. Unlike traditional methods, prediction,
accuracy and simplicity are in conflict.