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What can you infer about your model when the coefficient of determination is low?

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In the realm of Business Intelligence (BI), understanding the performance of predictive models is crucial. When you come across a low coefficient of determination, denoted as R-squared (R2), in your model's output, you're facing a signal that the model does not explain the variability of the response data around its mean effectively. R2 is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. A low R2 indicates that your model may not be capturing all the influential factors or that there is a high level of randomness in the dataset.

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Lakshmi Narayana Segu

Enterprise Cloud & Data Architect Delivery focused on Responsible Investments, Impact Investing and Artificial…
Maria Alice Jovinski

Product Lead at epharma | Ambassador ONA | People Analytics

1 R2 Basics

R2, ranging from 0 to 1, is a key metric in evaluating the goodness of fit for a model. If your model's R2 is low, it suggests that the independent variables you have included do not account for much of the variation in the dependent variable. This could mean that important predictors are missing, or that the data contains a high level of inherent variability that the model cannot capture. It's also possible that the model is too simple and cannot capture the complex relationships present in the data.

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Lakshmi Narayana Segu

Enterprise Cloud & Data Architect Delivery focused on Responsible Investments, Impact Investing and Artificial Intelligence
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A low coefficient of determination indicates that the model is not able to predict the output very well. This means that there is a need for revisiting the model which is used, data irregularities, assumptions and biases should be revisited. Further adjustments to the approaches taken, models used and data refinements done are required to take the next steps on achieving a higher fit. But shouldn’t look to get to overfitting the model as it could lead to making the model too specific and not so useful when some of the parameters change in real world.

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Walter Cipolla

Subject Matter Expert Salud LATAM en Global Hitss Argentina Harvard Medical School
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A low coefficient of determination indicates limited explanatory power in predicting the dependent variable. It suggests the model may not capture underlying relationships effectively, prompting the need for refinement. Analysts should revisit assumptions, identify biases, and explore additional variables for improved accuracy. This underscores the importance of cautious interpretation and ongoing model refinement in data analysis.

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Kashyapkumar Bhalerao Sr. Business System Analyst MBA CSPO? LIII

Sr. Specialist Consulting at - LTIMindtree
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coefficient of determination is low indicates that the model is not explaining a significant portion of the variation in the dependent variable. It is because Weak variable relationship model selection is not good variation is the data is too much and Complexity of the model in selection.

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2 Model Relevance

When faced with a low R2, question the relevance of your model. It could be that the model is not suitable for the data you're trying to analyze. For instance, if you're using a linear model but the relationship between variables is non-linear, your R2 will likely reflect this mismatch. In such cases, consider exploring other types of models that can better capture the underlying patterns in your data.

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3 Data Quality

Poor data quality can also lead to a low R2. If your dataset has errors, outliers, or missing values, these issues can distort the results of your analysis. Before drawing conclusions from your model, ensure that you've conducted thorough data cleaning and preprocessing. This might involve removing or correcting erroneous data points, filling in missing values, or applying transformations to reduce skewness and manage outliers.

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4 Additional Variables

A low R2 might indicate that there are additional variables that you need to include in your model. These could be interaction terms, where the effect of one predictor on the dependent variable depends on the level of another predictor, or entirely new variables that have a significant impact on the outcome you're trying to predict. You should revisit your domain knowledge and consider whether there are other factors that could explain the variance in your dependent variable.

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Obinna Ugochukwu

GIS Personnel and Statistician National Population Commission|| Mapper/Data Quality Assurance Personnel at RoadPreppers Technologies||Data Scientist ||Data Analyst
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I support this idea,as it helps to improve interactions with more variables,which in turn increase the accuracy of models.It is very important to revisit domain knowledge and consider including new factors to the variables which could improve models generally.

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5 Model Complexity

It's possible that your model is too simple to capture the complexity of the data. However, be cautious about overcomplicating your model. Adding more variables can increase R2 but may also lead to overfitting, where the model starts to capture noise instead of the underlying relationship. This reduces the model's ability to generalize to new data. Striking the right balance between model complexity and predictive power is key.

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6 Reflect and Iterate

A low R2 is not the end of your analysis—it's an opportunity to reflect and iterate on your model. Consider using it as a starting point for further exploration. You might look into different modeling techniques, add more data, or consult with domain experts to improve your understanding of the factors that influence your outcome variable. Remember, BI is an iterative process, and refining models is part of developing robust insights.

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Maria Alice Jovinski

Product Lead at epharma | Ambassador ONA | People Analytics
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In people analytics, a low R2 is just the start of the journey. By considering complex variables such as organizational climate, engagement, and performance metrics, we can significantly enrich the model over time. We need to prepare for scenario analysis and continuous data collection to make the analyses more comprehensive and robust each quarter.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What can you infer about your model when the coefficient of determination is low?

1

2

3

4

5

6

7

1 R2 Basics

2 Model Relevance

3 Data Quality

4 Additional Variables

5 Model Complexity

6 Reflect and Iterate

7 Here’s what else to consider

Business Intelligence

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What can you infer about your model when the coefficient of determination is low?

1

2

3

4

5

6

7

1 R2 Basics

2 Model Relevance

3 Data Quality

4 Additional Variables

5 Model Complexity

6 Reflect and Iterate

7 Here’s what else to consider

Business Intelligence

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