How can you ensure that your data is free of outliers before visualizing it?

An outlier is a data point that significantly deviates from other data in a set, being much higher or lower. Causes include measurement errors, data entry mistakes, sampling bias, or rare events. These points can skew averages and other statistics, affecting data analysis and model accuracy. Outliers should be identified and treated based on the context - they can be removed, adjusted, or analyzed separately. In some cases, outliers provide valuable insights and shouldn't be automatically disregarded. Their treatment is crucial for the integrity and accuracy of data analysis.

It all comes back to your controls for data collection. If you invest the time and effort to control your experimentation and data collection, you should see the benefits on the back end of analysis. You may still experience outliers when you perform your analytical and stochastic evaluations of your data. Questions regarding the integrity of the data can be addressed due to the documented and detailed process and controls used during the experimentation. Outliers that do arise now should lead to deeper insight and exploration.

You should not immediately dismiss an outlier as you can broadly classify them into two main categories in finance: the first type is the result of an erroneous process and the second one is coming from a valid series of business activities (take the case of extending a $100M facility when your average faculty size is ~$8M with a standard deviation of $5M). Although the latter point does qualify as an outlier, you do want to have it at least captured, reported, and analyzed.

Ensure data is free of outliers...? 1. You can't 2. You shouldn't, as has been mentioned by several others here 3. Often, outliers are the very things an analysis needs to capture. For example, I work for DTE Energy, a major regional electric and gas utility in Michigan. The focus of my work is forecasting damage to the electrical system from storms. So, modeling and visualizing outliers is pretty much all I do. Power outages are outliers, only happening a few days a year. Cancer is an outlier, only present in a few people tested for it. Car accidents are outliers, only happening in a tiny fraction of all trips in an automobile. Detecting, predicting, and visualizing outliers is the primary work of much of statistics and data science.

Outliers are points that are potentially formed by a different statistical or other pattern than other data points. They need not be higher or lower than other points, but could be merely different in a univariate or multivariate sense.

Vamos lá: - Gráfico de caixa: usa quartis para definir limites normais. Valores fora dos limites são outliers. - Pontuação Z: mede distância da média em desvios padrão. Valores muito altos ou baixos são outliers. - Método de agrupamento: usa algoritmos para formar grupos. Valores isolados ou distantes são outliers. - Abordagem gráfica: usa gráficos para visualizar dados. Valores que fogem dos padrões são outliers.

You need to have a thorough understadning of the underlying business activity when applicable. This is one of the best starting points before picking a methodology. After that, the choice of your method depends on the underlying datasets with which you are working: For example, seasonal decomposition of time series (STL), Exponential Smoothing State Space Models (ETS), and Autoregressive Integrated Moving Average (ARIMA) are the ones that you can deploy on Time Series. STL decomposes time series into seasonal, trend, and residual components, making it easier to identify outliers. ETS framework allows for the identification and analysis of "Error" and "Trend" components " separately, making it possible to detect outliers.

Some excellent answers have been suggested already. These are mainly statistical in nature. In the context of large data, AI and machine learning, other approaches are also available. Unlike the traditional methods, relying on the IQR, these can identify outliers even for complex, high-dimensional data types. To understand one such method, think of a decision tree. For each variable, it splits the data. For instance, it can split it in "age > 30" and "age <= 30". An outlier can be separated from the remainder of the data in relatively few such splits. For example, if someone's age is 115, they can likely be isolated from all other individuals in a single split. Isolation forests leverage this to identify outliers.

A good rule of thumb is to mark any point that is more than 3 times the Interquartile Range (IQR) as an outlier. This ofcourse depends on subject matter expertise and one’s own gut about the problem or prior knowledge of the domain. When you are modeling rare events then outliers are what you want to detect!

I have found that the Modified Z-Score (Mod Z-Score) stands out as a more robust alternative to the traditional Z-score, and is particularly useful for datasets with skewness. It differs from the Z-score by utilizing the median and Median Absolute Deviation (MAD) instead of the mean and standard deviation, which makes it more suitable for skewed distributions and smaller datasets, and provides a more effective method for outlier detection across various data distributions, not just those that are normally distributed. Although it is slightly more complex and not as widely used as the Z-score, the Mod Z-Score is highly recommended for datasets where traditional methods may be inadequate due to the data's nature or the sample size.

Once you have detected outliers in your data, you need to decide how to handle them. There are a number of different approaches, depending on the specific data set and the analysis that you are conducting. Here are some common methods for handling outliers: . Remove the outliers; . Winsorize the outliers; . Transform the data; . Model the outliers,

It is important to understand the underlying mechanism that generated the outlier before doing anything to it. Outliers may be mistakes (data entry error, programming error), but they can also be legitimate observations that contribute to the story you are trying to tell. Before replacing or dropping an outlier, make sure you can explain it. An outlier may be the most interesting piece of your data, and if so, the last thing you want to do is drop it or replace it. If fact, a legitimate outlier may be the impetus for a whole new project.

Once we address an outlier in a column, particularly when training machine learning models, it is possible to introduce a flag column to signify whether the value underwent treatment or not. This approach allows us to furnish the model with supplementary information regarding the data.

According to the context, nature of data we can handle outliers differently. Initially we need to understand how it occurred, is it an data collection error, is it an measurement issue, etc. In some cases we can replace, remove or adjust outliers to improve the accuracy of our analytical results. We can transfor outliers to reduce their impact. In some cases according to modelling methods we can use data without handling outliers. Random Forests decision tree models can handle outliers to some extent. Robust linear regression is less sensitive to outliers compare to standard linear regression. In some cases we can remain outliers if they are indicating important information. We can use weighting to handle outliers in time series data.

You can pick the right method, depending on the context of you data analytic project, nature of outliers and the impact on your goals. Transformation methods (such as Scaling, Log Transformation or Box-Cox Transformation) can be useful, but be cautious of their impact on the underlying distributions as they aim to reduce the influence of extreme values.

One simple option is removing them, but this might lead to losing valuable information. Transformation methods offer a way to reduce outlier impact without throwing away data, though they can make interpretation more complex. Imputation, filling in missing values, introduces some uncertainty. Winsorizing, capping extreme values, alters the data distribution. Robust statistical methods are less bothered by outliers but might be more complex. Model-based approaches, considering overall data relationships, come with assumptions and added complexity. The best choice depends on your specific dataset and analysis goals, so it's crucial to decide based on your unique context

Having too many outliers can distort statistical analysis results. They may misrepresent key quantities, such as measures of center (especially the mean) or spread (e.g. standard deviation). However, in some cases they may contain crucial information. This should not be discounted. As an example, suppose that a life sciences team wants to develop a prognostic model for a rare disease. Due to its rare nature, patients are necessarily rare in a medical database. To find common properties between the rare disease patients, it is therefore crucial to identify the outliers. How they differ from less exceptional patients may be key to identifying risk factors and cohorts.

By visually inspecting the data using plots, or applying statistical methods like the Interquartile Range, most importantly by understanding the data's context, decide whether to remove, adjust, or keep outliers, and then re-visualize the data for accurate analysis.

The best way to choose the best option is to consider all of your options carefully and to weigh the pros and cons of each option. Here are some tips: . Identify your goals; . Gather information; . Compare the options: Once you have gathered information about each option, compare the options side-by-side. This will help you to identify the pros and cons of each option and to make a more informed decision. . Get feedback from others. Talk to people who have experience with different options. They can provide you with valuable insights and help you to make a better decision. . Trust your gut.

In some scenario, outlier is very important to consider. Ex: Use case which has 'salary' as one of the input features, Senior Manager/Director salary also taken into consideration though their salary is 10x more than the average salary of the rest of the employees in the organization. In that case, apply normalization and include the feature for prediction.

Some of the most common methods include: . Histograms: Histograms show the distribution of the data by plotting the frequency of each data point. This can help to identify outliers and to see how the outliers have affected the distribution of the data. . Boxplots: Boxplots show the median, quartiles, and outliers of the data. This can help to identify outliers and to see how the outliers have affected the distribution of the data. . Scatter plots: Scatter plots show the relationship between two variables by plotting each data point as a point on a graph. This can help to identify outliers and to see how the outliers have affected the relationship between the two variables.

We can decide for the best option by evaluating their impact on analysis and modelling methods that we plan to use. Removing outliers, changing value of outliers, replacing extreme values with less extreme values, transforming data – there are several options.

Choosing the best option for handling outliers involves considering the data's nature, analysis goals, and domain knowledge, evaluating outlier frequency and data type, and deciding based on the analysis's purpose, whether to keep, highlight, adjust, or remove outliers, with each approach having implications on data quality, pattern revelation, hypothesis testing, or model performance.

In practice, many datasets are complex and have many dimensions. While histograms, scatter plots, and other visualizations can then still be applied. However, they can only visualize one or two variables at a time. This is unfortunate, as it may not show the relations between the variables. One way to deal with this is by using a dimensionality reduction method before visualizing. These represent the information present in the dataset in fewer variables, making it easier to visualize. The most influential dimensionality reduction method is principal component analysis (PCA). Recently, AI-based approaches have also been gaining traction, including contrastive learning.

I find violin plots also useful since they visualize the distribution of the data alongside the box-and-whisker plot. From this visualization, you can intuitively gauge how removal or modification of outliers would affect the overall distribution of the data, if applicable.

80-20 rule always. Unless it's a deep financial analysis where every decimal matters , worth keeping it simple in dealing with outliers and leveraging them more from a narrative perspective

Beyond handling outliers, consider the broader implications of your data analysis. This includes understanding the potential biases introduced by outlier management, maintaining data integrity, and documenting your process for transparency. Additionally, stay open to the possibility that outliers might indicate new trends or hidden patterns. Regularly reviewing and updating your analysis techniques is also key, ensuring they align with evolving data landscapes and industry best practices.

Ignorar los valores atípicos puede llevar a conclusiones erróneas, afectando tanto la validez académica como las decisiones empresariales. Por ello, analizar los valores y su impacto en el conjunto de datos es esencial. En la visualización de datos, es importante mostrar los valores atípicos de forma que resalten sin distorsionar el conjunto de datos, permitiendo identificar tendencias, patrones y anomalías de manera clara. ESTOS VALORES DE SER CORRECTOS, TAMBIÉN PUEDEN CONTAR UNA HISTORIA, QUE POTENCIALMENTE EXPLICAN FENÓMENOS NO CONSIDERADOS.

What conclusions will be reached / not reached from the data? This perspective has to be considered first (imo) In other words why was the data collected ?

The most informative data which have the highest entropy and also random data has the highest entropy. So an important variable to a model could produce data which may look random. So it is really critical to know if the outliers are not causally related e.g. using something like convergent cross mapping or Granger causality approach.