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Machine Learning FAQ

E.g., “keep all features that have a variance greater or equal to x” or “keep the the top k features with the largest variance.” We assume that features with a higher variance may contain more useful information, but note that we are not taking the relationship between feature variables or feature and target variables into account, which is one of the drawbacks of filter methods.

A Wrapper Method Example: Sequential Feature Selection Sequential Forward Selection (SFS), a special case of sequential feature selection, is a greedy search algorithm that attempts to find the “optimal” feature subset by iteratively selecting features based on the classifier performance.

Through adding the L1 term, our objective function now becomes the minimization of the regularized cost, and since the penalty term grows with the value of the weight parameters (λ is just a free parameter to fine-tune the regularization strength), we can induce sparsity through this L1 vector norm, which can be considered as an intrinsic way of feature selection that is part of the model training step.

7 Ways to Improve your Predictive Models

This is a figure I dug up from an old slide deck I prepared years ago for a workshop on predictive modeling.

This is also known as over-fittingand means that your model is too flexible for the amount of training data you have and ends up picking up noise in addition to the signal, learning random patterns that happen by chance and do not generalize beyond your training data.

Some highly accurate models could be very hard to deploy in production environments and are usually black boxes that are very hard to interpret or debug, so many production systems opt for simpler, less accurate model that are less resource-intensive, easier to deploy and debug.

Why, How and When to apply Feature Selection

Modern day datasets are very rich in information with data collected from millions of IoT devices and sensors.

This makes the data high dimensional and it is quite common to see datasets with hundreds of features and is not unusual to see it go to tens of thousands.

When presented data with very high dimensionality, models usually choke because Feature Selection methods helps with these problems by reducing the dimensions without much loss of the total information.

The least square errors in both the models are compared and checks if the difference in errors between model X and Y are significant or introduced by chance.

A highly correlated feature is given higher score and less correlated features are given lower score.

8 Proven Ways for improving the “Accuracy” of a Machine Learning Model

But, if you follow my ways (shared below), you’d surely achieve high accuracy in your models (given that the data provided is sufficient to make predictions).

In this article, I’ve shared the 8 proven ways using which you can create a robust machine learning model.

The model development cycle goes through various stages, starting from data collection to model building.

This practice usually helps in building better features later on, which are not biased by the data available in the data-set.

The unwanted presence of missing and outlier values in the training data often reduces the accuracy of a model or leads to a biased model.

It shows that, in presence of missing values, the chances of playing cricket by females is similar as males.

But, if you look at the second table (after treatment of missing values based on salutation of name, “Miss”

This step helps to extract more information from existing data. New information is extracted in terms of new features. These features may have a higher ability to explain the variance in the training data.

Feature Selection is a process of finding out the best subset of attributes which better explains the relationship of independent variables with target variable.

Hitting at the right machine learning algorithm is the ideal approach to achieve higher accuracy.

To tune these parameters, you must have a good understanding of these meaning and their individual impact on model. You can repeat this process with a number of well performing models.

This technique simply combines the result of multiple weak models and produce better results. This can be achieved through many ways: To know more about these methods, you can refer article “Introduction to ensemble learning“.

Till here, we have seen methods which can improve the accuracy of a model. But, it is not necessary that higher accuracy models always perform better (for unseen data points). Sometimes, the improvement in model’s accuracy can be due to over-fitting too.

To know more about this cross validation method, you should refer article “Improve model performance using cross validation“.

Once you get the data set, follow these proven ways and you’ll surely get a robust machine learning model. But, these 8 steps can only help you, after you’ve mastered these steps individually.

If you need any more help with machine learning models, please feel free to ask your questions in the comments below.

The 10 Statistical Techniques Data Scientists Need to Master

Regardless of where you stand on the matter of Data Science sexiness, it’s simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it.

With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers — and the companies that hire them — Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress.

As Josh Wills put it, “data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.” I personally know too many software engineers looking to transition into data scientist and blindly utilizing machine learning frameworks such as TensorFlow or Apache Spark to their data without a thorough understanding of statistical theories behind them.

Now being exposed to the content twice, I want to share the 10 statistical techniques from the book that I believe any data scientists should learn to be more effective in handling big datasets.

I wrote one of the most popular Medium posts on machine learning before, so I am confident I have the expertise to justify these differences: In statistics, linear regression is a method to predict a target variable by fitting the best linear relationship between the dependent and independent variable.

Now I need to answer the following questions: Classification is a data mining technique that assigns categories to a collection of data in order to aid in more accurate predictions and analysis.

Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.

Types of questions that a logistic regression can examine: In Discriminant Analysis, 2 or more groups or clusters or populations are known a priori and 1 or more new observations are classified into 1 of the known populations based on the measured characteristics.

Discriminant analysis models the distribution of the predictors X separately in each of the response classes, and then uses Bayes’ theorem to flip these around into estimates for the probability of the response category given the value of X.

In other words, the method of resampling does not involve the utilization of the generic distribution tables in order to compute approximate p probability values.

In order to understand the concept of resampling, you should understand the terms Bootstrapping and Cross-Validation: Usually for linear models, ordinary least squares is the major criteria to be considered to fit them into the data.

This approach fits a model involving all p predictors, however, the estimated coefficients are shrunken towards zero relative to the least squares estimates.

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables.

So far, we only have discussed supervised learning techniques, in which the groups are known and the experience provided to the algorithm is the relationship between actual entities and the group they belong to.

Below is the list of most widely used unsupervised learning algorithms: This was a basic run-down of some basic statistical techniques that can help a data science program manager and or executive have a better understanding of what is running underneath the hood of their data science teams.

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