Example: Binary classification¶

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This example shows how to use ATOM to solve a binary classification problem. Additonnaly, we'll perform a variety of data cleaning steps to prepare the data for modeling.

The data used is a variation on the Australian weather dataset from Kaggle. You can download it from here. The goal of this dataset is to predict whether or not it will rain tomorrow training a binary classifier on target RainTomorrow.

Load the data¶

In [13]:

# Import packages
import pandas as pd
from atom import ATOMClassifier

# Import packages import pandas as pd from atom import ATOMClassifier

In [14]:

# Load data
X = pd.read_csv("docs_source/examples/datasets/weatherAUS.csv")

# Let's have a look
X.head()

# Load data X = pd.read_csv("docs_source/examples/datasets/weatherAUS.csv") # Let's have a look X.head()

Run the pipeline¶

In [15]:

# Call atom using only 5% of the complete dataset (for explanatory purposes)
atom = ATOMClassifier(X, y="RainTomorrow", n_rows=0.05, n_jobs=8, verbose=2)

# Call atom using only 5% of the complete dataset (for explanatory purposes) atom = ATOMClassifier(X, y="RainTomorrow", n_rows=0.05, n_jobs=8, verbose=2)

In [16]:

# Impute missing values
atom.impute(strat_num="median", strat_cat="drop", max_nan_rows=0.8)

# Impute missing values atom.impute(strat_num="median", strat_cat="drop", max_nan_rows=0.8)

In [17]:

# Encode the categorical features
atom.encode(strategy="Target", max_onehot=10, infrequent_to_value=0.04)

# Encode the categorical features atom.encode(strategy="Target", max_onehot=10, infrequent_to_value=0.04)

In [18]:

# Train an Extra-Trees and a Random Forest model
atom.run(models=["ET", "RF"], metric="f1", n_bootstrap=5)

# Train an Extra-Trees and a Random Forest model atom.run(models=["ET", "RF"], metric="f1", n_bootstrap=5)

Analyze the results¶

In [19]:

# Let's have a look at the final results
atom.results

# Let's have a look at the final results atom.results

In [20]:

# Visualize the bootstrap results
atom.plot_results(title="RF vs ET performance")

# Visualize the bootstrap results atom.plot_results(title="RF vs ET performance")

In [21]:

# Print the results of some common metrics
atom.evaluate()

# Print the results of some common metrics atom.evaluate()

In [22]:

# The winner attribute calls the best model (atom.winner == atom.rf)
print(f"The winner is the {atom.winner.name} model!!")

# The winner attribute calls the best model (atom.winner == atom.rf) print(f"The winner is the {atom.winner.name} model!!")

In [23]:

# Visualize the distribution of predicted probabilities
atom.winner.plot_probabilities()

# Visualize the distribution of predicted probabilities atom.winner.plot_probabilities()

In [24]:

# Compare how different metrics perform for different thresholds
atom.winner.plot_threshold(metric=["f1", "accuracy", "ap"], steps=50)

# Compare how different metrics perform for different thresholds atom.winner.plot_threshold(metric=["f1", "accuracy", "ap"], steps=50)