Supervised Learning in Machine Learning > Data Preprocessing for Machine Learning

Splitting Dataset

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Training, Cross Validation, and Test sets are all typical best practices. This allows you to fine-tune the algorithm's parameters without making decisions based solely on training data.

Splitting a dataset may also be useful for determining whether your model is suffering from one of two extremely prevalent problems known as underfitting or overfitting. Underfitting is typically caused by a model's inability to contain the relationships between data.

Data splits are important in machine learning because they help in improving the quality of a model by providing an opportunity for evaluation against another set of data that were not used to train it. Splitting data into two sets helps in understanding how well it was trained, which helps in making better decisions about what type of model should be built next time around.

The three most common split methods are:

  • Stratified sampling - this method involves splitting the data into two groups, then taking a random sample from each group;
  • Random sampling - this method involves taking a random sample from each group; and
  • Bootstrapping - this method involves taking an initial sample from each group and then re-sampling with replacement until all groups have been sampled.

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Muhammad Fahad Bashir
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Explained the concept easily

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Pradeep Kumar Kaushik
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Please give me iris,csv file.

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Ankit Malik
4

where is the finaldata.csv

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Vimal Bhatt
5

great learning plateform kushal sir is really too good

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good

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Prabhat Yadav
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Superb course content and easy to understand.

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fahad ameer
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good

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Course Content

  1. Introduction to Machine Learning
    1m 5s
  2. Environment Setup part 1
    3m 26s
  3. Environment Setup part 2
    4m 43s
  4. Environment Setup part 3
    2m 33s

  1. Data Wrangling
    12m 26s
  2. Importing Libraries and Dataset
    11m 19s
  3. Handling Missing Data
    11m 51s
  4. Handling Missing Data - Practical
    17m 3s
  5. Encoding Categorical Data
    6m 34s
  6. Encoding Catergorical Data - Practical
    8m 3s
  7. Splitting Dataset
    3m 38s
  8. Splitting Dataset - Practical
    9m 36s
  9. Normalizing the Data - Part 1
    6m 18s
  10. Normalizing the Data - Part2
    6m 58s
  11. Finding Machine Learning Datasets
    9m 17s
  12. Exploratory Data Analysis
    9m
  13. Plotting Graphs - Part 1
    19m 12s
  14. Plotting Graphs - Part 2
    23m 44s
  15. Distribution Models - Part 1
    33m 54s
  16. Distribution Models - Part 2
    13m 56s
  17. Assignment of Data Preprocessing for Machine Learning
    1m 39s

  1. Machine Learning Paradigms
    9m 53s
  2. Sampling Methods
    19m 43s
  3. Underfitting and Overfitting in Models
    6m 7s
  4. Variance and Bias
    9m 49s
  5. Assignment of Machine Learning Paradigms
    3m 24s

  1. Overview of Classification
    5m 22s
  2. Logistic Regression
    4m 54s
  3. Logistic Regression - Practical
    32m 40s
  4. KNN
    7m 21s
  5. KNN - Practical
    35m
  6. Decision Trees for Classification
    6m 17s
  7. Decision Trees Practical - 1
    22m 41s
  8. Decision Trees Practical - 2
    20m 42s
  9. Random Forest
    8m 37s
  10. Support Vector Machine Classifier
    5m 44s
  11. Support Vector Machine Classifier - Practical 1
    16m 59s
  12. Support Vector Machine Classifier - Practical 2
    11m 18s
  13. Naive Bayes Classifier
    5m 25s
  14. Naive Bayes Classifier - Practical 1
    15m 21s
  15. Naive Bayes Classifier - Practical 2
    11m 7s
  16. Evaluating Classification Models Performance
    7m 31s
  17. Evaluating Classification Models Performance - Practical
    33m 2s
  18. Assignment of Supervised Learning Algorithms
    1m 12s

  1. Simple Linear Regression
    8m 57s
  2. Simple Linear Regression - Practical
    12m 34s
  3. Multi-Linear Regression
    3m
  4. Support Vector Regressor
    10m 47s
  5. Support Vector Regressor - Practical
    6m 47s
  6. Decision Tree Regressor
    4m 52s
  7. Decision Tree Regressor - Practical
    5m 2s
  8. Regressor Model Selection
    9m 7s
  9. Evaluating Regression Model Performance
    8m 45s
  10. Evaluating Regression Model Performance - Practical
    11m 34s
  11. Assignment of Regression Algoritms
    1m 12s

  1. Advance Trends in Machine Learning
    10m 13s

  1. Course Summary
    3m 51s

  1. Interview Questions Part 1
    19m 22s
  2. Interview Questions Part 2
    12m 23s
  3. Interview Questions Part 3
    15m 20s

  1. Career Guidelines
    1m 33s
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