How to use default Hyperparameters In Blog
In the world of Data Science, algorithms can’t automatically figure out the best way to make predictions. There are certain values called hyperparameters that can be adjusted in the algorithm to get better results. Using default parameters means, using the same parameters given by the algorithm. Hyperparameters are externally set by an algorithm. Internal parameters are used while training the data. External parameters are set by the user before the training process begins. Hyperparameters influence the performance of the algorithm.
Causes of Using Default Parameters
Example: Baseline Performance assessment : Let’s consider using a decision tree for a classification task. We always use default parameters to get the initial accuracy. Then we will experiment with different values to get better results. Using the initial value, without experimenting with other values while training a model will give us poor results.
Key Aspects of Hyperparameter
- Learning Rate: It says about the size of the steps the model takes during the optimization process. Larger learning rates will never converge whereas the smaller learning rates will take more time to converge. We should experiment with different learning rate values to get optimized results.
- Regularization Strength: To reduce high variance we need more training data. It is expensive. Regularization can be considered as an alternate method to increase performance. In regularization, we add an extra parameter, lambda to the cost function. If lambda is zero there will be no regularization and higher lambda values correspond to more regularization.
- Hidden Layers: It is a hyperparameter tuning method used in Neural Networks. Smaller hidden layers are enough for simpler problems whereas larger problems need more hidden layers. Using the right number of hidden layers will prevent overfitting. The number of layers can be tuned using the ‘for loop’.
- Max depth in Decision Tree: It is the longest path between the root node and the leaf node. Increasing the depth of the tree increases the performance. On the other hand, when max_depth increases initially but after a certain point it decreases rapidly.
Practical Tips
- By conducting hyperparameter tuning using various techniques like grid search or random search to find optimal values.
- Understand the impact of key hyperparameters such as learning rate, regularization strength, hidden layers, and max depth in decision trees.
6 Common Mistakes to Avoid in Data Science Code
As we know Data Science is a powerful field that extracts meaningful insights from vast data. It is our job to discover hidden secrets from the available data. Well, that is what data science is. In this world, we use computers to solve problems and bring out hidden insights. When we enter into such a big journey, there are certain things we should watch out for. Those who like playing with data know the tricky part of understanding the data and the possibility of making mistakes during the data processing.
How can I avoid mistakes in my Data Science Code?
How can I write my Data Science code more efficiently?
To answer all your questions, In this article, you get to know Six common mistakes to avoid in data science code in detail.
Table of Content
- Ignoring Data Cleaning
- Neglecting Exploratory Data Analysis
- Ignoring Feature Scaling
- Using default Hyperparameters
- Overfitting the Model
- Not documenting the code
- Conclusion
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