This weblog submit explores a machine studying algorithm that makes use of a set of determination timber to categorise take a look at objects “Random Forest Classifier”
Right here we’re utilizing coronary heart illness knowledge set , you may take any dataset from Kaggle.
Firstly, lets import all of the necessary libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt%matplotlib inline
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import RandomizedSearchCV
Now load the information
df = pd.read_csv("/content material/heart-disease.csv")
Let’s Carry out EDA (exploratory knowledge evaluation ) on our knowledge set :
df.head()
df.goal.value_counts().plot(sort="bar",coloration=["salmon","lightblue"]);plt.determine(figsize=(10,6));
plt.scatter(df.age[df.target==1],
df.thalach[df.target==1],coloration="salmon");
plt.scatter(df.age[df.target==0],
df.thalach[df.target==0],coloration="lightblue");
# Add some useful information
plt.title("Coronary heart Illness in perform of Age and Max Coronary heart Charge")
plt.xlabel("Age")
plt.ylabel("Max Coronary heart Charge")
plt.legend(["Disease","No Disease"]);
Making a correlation matrix :
corr_matrix = df.corr()fig,ax = plt.subplots(figsize=(15,10))
ax = sns.heatmap(corr_matrix,
annot=True,
linewidth=0.5,
fmt=".2f");
Splitting the information into prepare and take a look at :
X = df.drop("goal",axis=1)
y = df["target"]np.random.seed(42)
X_train ,X_test , y_train , y_test = train_test_split(X,y,test_size=0.2)
test_size=0.2 splits knowledge into 80:20 ratio , 80% of trainning knowledge and 20% of take a look at knowledge
Becoming the information
mannequin = RandomForestClassifier()
mannequin.match(X_train,y_train)
Checking the rating:
mannequin.rating(X_test,y_test)
HyperParameter Tuning :
rf_grid = {"n_estimators":np.arange(10,1000,50),
"max_depth":[None,3,5,10],
"min_samples_split":np.arange(2,20,2),
"min_samples_leaf":np.arange(1,20,2)}rs_log_reg = RandomizedSearchCV(LogisticRegression(),
param_distributions = log_reg_grid,
cv=5,
n_iter=20,
verbose=True)
# Match random hyperparameter for Logistic regression
rs_log_reg.match(X_train,y_train)
Checking the very best rating after hyperparameter tuning :
rs_rf.rating(X_test,y_test)
Thus , you carried out random forest classifier
To know extra intimately about Random Forest Classifier :
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