Machine Learning allows organizations to make better data-driven decisions. It also helps solve machine learning mistakes that were previously beyond the reach of traditional analytical methods. Machine learning isn’t magic. Machine learning presents many of the same challenges as other analytics methods. We will discuss some common machine learning mistakes organizations make when incorporating machine learning into their analytics strategy.
Machine Learning Mistakes 1: Planning machine learning programs without data scientists
A shortage of deep analytics talent is a constant problem. The need for people who can consume and manage analytical content is even more pressing. Many organizations have made it a priority to recruit and retain these highly-skilled technical professionals.
Data scientists are the most highly skilled analytics professionals and require a unique combination of mathematics, computer science, and domain expertise. Data scientists are highly sought after and command high salaries.
How to solve it?
- Develop an analytics center of excellence: These centers act as internal analytics consulting firms. This center allows for efficient use of analytical skills throughout the company and consolidates analytical talent.
- Build relationships with universities: To find new talent, create an internship program or university recruitment program. To solve problems, you can also use university programs to pair students with companies.
- Build talent from within: Search for people who are naturally gifted in mathematics and problem solving and invest in data science training.
- Make analytics easier to use: Other people in the company can also solve data problems using your visualization tools, and not just data scientists.
Machine Learning Mistakes 2: Starting without good data
Although improving algorithms may be seen as the glamorous side of machine learning technology, it is actually a time-consuming task that involves preparing data and dealing directly with quality issues. To get accurate results from your models, data quality is crucial. Data quality issues include:
- Noisy data: Data that contains large amounts of misleading or conflicting information.
- Sparse data: Data with missing, inconsistent, or erroneous character features, categorical, and categorical values.
- Insufficient data: Data with very few actual values. Data made up mostly of zeros and missing values.
- Inadequate data: Uncomplete or biased data.
Data collection and storage can lead to many problems. However, there are steps you can take to minimize these issues.
How to solve it?
- Data security governance: Consider data security concerns at the start of any machine learning project, particularly if you need support from other departments. Data governance plans should also be made early to consider how algorithms will work, be stored, and then reused.
- Data exploration: Professional machine learning exercises that are productive and specific to a business need should be started with quantifiable results. Data scientists need to be able to query, summarize, visualize, and create algorithms for new data.
- Data integration and preparation: After data is collected and cleaned up, it needs to be converted into a format that machine learning algorithms can use.
Machine Learning Mistakes 3: Insufficient infrastructure for machine learning
Most organizations find managing machine learning infrastructure can be a daunting task. Reliable relational database management systems that are reliable can be overwhelmed by the volume and variety of data organizations need to analyze and collect.
How to solve it?
These areas are important to plan for in order to ensure that your infrastructure can handle machine learning.
- Flexible storage: Create an organization-wide storage solution that meets all data requirements and allows for technological advancements. Considerations for storage should include data structure, digital footprint, and usage.
- Powerful computation: Data scientists can cycle through many data preparation techniques and models in order to find the best solution quickly and easily with a secure, scalable, and reliable computing infrastructure. These are some of the most successful approaches to machine learning.
- Hardware acceleration: Solid-state hard drives (SSDs) are best for I/O-intensive tasks like data preparation and disk-enabled analytic software. Graphic Processing Units (GPUs) are used for computationally intensive tasks such as matrix algebra that can be done in parallel.
- Distributed computing: This is a distributed computing environment. Data and tasks are often split among many computers. This can reduce execution times. You should ensure that you use a distributed environment that is well-suited to machine learning.
- Elasticity: Machine learning can make storage and compute resource consumption highly dynamic, with high volumes at certain times and low quantities at others. Infrastructure flexibility allows for better use of limited computational resources, and/or financial expenses.
Machine Learning Mistakes 4: Applying Machine Learning too soon or without a strategy
Many organizations that are data-driven have spent many years creating successful analytics platforms. It can be difficult to decide when to include newer, more complicated modeling methods in an overall analytics strategy. The transition to machine-learning techniques might not be necessary until IT or business requirements change. Complex machine learning models can be difficult to interpret, document, and justify in regulated industries.
How to solve it?
Machine learning can be seen as an extension of existing analytical processes and other decision-making tools. A bank might use traditional regression for its regulated transactions, but use a more precise machine learning technique to determine when the regression model needs to be updated.
Several innovative methods have been proven to be effective for companies that want modern machine learning.
- Anomaly detection: There are many machine algorithms that can detect anomalies, fraud, and outliers.
- Segmented model factories: Markets can have very different segments. In health care, each patient within a treatment group may require special attention. These cases may be better served by applying different predictive modeling to each segment or patient. This will allow for more targeted and efficient actions. A model factory approach allows you to automatically build models across multiple segments or individuals, allowing for any improvements in accuracy and efficiency.
- Ensemble models: Combining results from multiple models or several models can produce better predictions than using one model. Ensemble modeling algorithms, such as super learners, gradient boosting machines, and random forests, have proven to be very promising. However, it is possible to improve the results by using pre-existing models in custom combinations.
Machine Learning Mistakes 5: Difficulties interpreting or sharing model methodologies
Machine learning algorithms are difficult to understand, but they can be used as excellent predictors. They are also complex. The problem with machine learning algorithms is that they are often viewed as black boxes. Models must be explicable in order to meet regulatory requirements in certain industries like banking and insurance.
How to solve it?
Some interpretability problems can be solved by combining traditional and machine-learning techniques. A hybrid strategy could work. Examples of hybrid strategies are:
- Advanced Regression Techniques: It is important to know when to use advanced techniques. Penalized regression techniques, for example, are well-suited to large data. You can fine-tune the trade-off between accuracy and interpretability with generalized additive models. Quantile regression allows you to fit a linear model that is interpretable to different percentiles. This makes it possible to create different variables to model different behaviors.
- Using machine-learning models as benchmarks: Machine learning models are different from traditional linear models in that they take into account a lot of implicit variable interactions. Your regression model may be less accurate than your machine-learning model. This means that you might have missed important interactions.
- Surrogate model: surrogate models can be used to interpret complex models. Fit a machine learning model to the training data. Next, train an interpretable, traditional model using the original training data. However, instead of using the target from the training data as the target, you can use the predictions from the more complicated algorithm to create this model.
Machine learning is a powerful tool for business. It requires you to understand machine learning within broader analytics, learn proven machine learning applications, anticipate the challenges that may arise from machine learning in your organization, and learn from experts in the field.