Discover and read the best of Twitter Threads about #Overfitting
Most recents (3)
1/🧵✨Occam's razor is a principle that states that the simplest explanation is often the best one. But did you know that it can also be applied to statistics? Let's dive into how Occam's razor helps us make better decisions in data analysis. #OccamsRazor #Statistics #DataScience
2/ 📏 Occam's razor is based on the idea of "parsimony" - the preference for simpler solutions. In statistics, this means choosing models that are less complex but still accurate in predicting outcomes. #Simplicity #DataScience
3/ 📊 Overfitting is a common problem in statistics, where a model becomes too complex and captures noise rather than the underlying trend. Occam's razor helps us avoid overfitting by prioritizing simpler models with fewer parameters. #Overfitting #ModelSelection #DataScience
The model will not perform well if training data is small, or noisy with errors outlier or if #data is not representative consist of irrelevant feature(garbage in garbage out) lastly neither too simple(result in #underfitting) nor too complex(results in #overfitting
#DataScience
#DataScience
Not enough training data.
Poor Quality of data.
Irrelevant features.
Nonrepresentative training data.
Overfitting and Underfitting.
#DataCleaningchallenge #MachineLearning #Deeplearning #DataAnalytics #DataVisualization #Python #Powerbi #SQL #MYSQL
analyticsvidhya.com/blog/2021/06/5…
Poor Quality of data.
Irrelevant features.
Nonrepresentative training data.
Overfitting and Underfitting.
#DataCleaningchallenge #MachineLearning #Deeplearning #DataAnalytics #DataVisualization #Python #Powerbi #SQL #MYSQL
analyticsvidhya.com/blog/2021/06/5…
#SupervisedLearning is a type of #MachineLearning where an algorithm is trained on a labeled dataset to predict outcomes for new data
The labeled dataset contains input variables and the desired output, and the algorithm uses this information to make predictions
The labeled dataset contains input variables and the desired output, and the algorithm uses this information to make predictions
In #SupervisedLearning, the algorithm is constantly adjusting its parameters to minimize the prediction error
One of the most popular algorithms for #SupervisedLearning is #LinearRegression, used for prediction problems where the target is continuous
One of the most popular algorithms for #SupervisedLearning is #LinearRegression, used for prediction problems where the target is continuous
Another common algorithm is #LogisticRegression, used for classification problems where the target is binary
#DecisionTrees and #RandomForests are commonly used for both regression and classification problems
#DecisionTrees and #RandomForests are commonly used for both regression and classification problems
